{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe336daf-1792-4bb8-98d5-e45bfe6a083a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import  Counter\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import gc\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "#设置显示中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#设置正常显示符号\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a57ae34d-db7a-439c-a7b0-51e0f7744c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.read_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\test_format1.csv')\n",
    "train_data=pd.read_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\train_format1.csv')\n",
    "user_info=pd.read_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\user_info_format1.csv')\n",
    "user_log=pd.read_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\user_log_format1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a096adc9-fda9-4a9a-884b-e33c0b61e0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_men_usage(df,verbose=True):\n",
    "    start_mem=df.memory_usage().sum() / 1024 ** 2\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtypes\n",
    "        if col_type in numerics:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3]=='int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)\n",
    "        else:\n",
    "            if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                df[col] = df[col].astype(np.float16)\n",
    "            elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                df[col] = df[col].astype(np.float32)\n",
    "            else:\n",
    "                df[col] = df[col].astype(np.float64)\n",
    "\n",
    "    end_mem=df.memory_usage().sum() / 1024 ** 2\n",
    "    print('Memory usage after optimization is:{:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format((end_mem - start_mem) * 100/start_mem))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "abbc0a8a-b299-446f-b7af-108c10a01916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage after optimization is:1.74 MB\n",
      "Decreased by -70.8%\n",
      "Memory usage after optimization is:3.49 MB\n",
      "Decreased by -41.7%\n",
      "Memory usage after optimization is:8.09 MB\n",
      "Decreased by -16.7%\n",
      "Memory usage after optimization is:1204.76 MB\n",
      "Decreased by -58.9%\n"
     ]
    }
   ],
   "source": [
    "train_data=reduce_men_usage(train_data)\n",
    "test_data=reduce_men_usage(test_data)\n",
    "user_info=reduce_men_usage(user_info)\n",
    "user_log=reduce_men_usage(user_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "efe837d8-fbb8-437e-9dde-9a930c07fb50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  label\n",
       "0    34176         3906      0\n",
       "1    34176          121      0\n",
       "2    34176         4356      1\n",
       "3    34176         2217      0\n",
       "4   230784         4818      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4231610c-3a79-455a-998a-923a8ffcdf41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163968</td>\n",
       "      <td>4605</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>360576</td>\n",
       "      <td>1581</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98688</td>\n",
       "      <td>1964</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98688</td>\n",
       "      <td>3645</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>295296</td>\n",
       "      <td>3361</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  prob\n",
       "0   163968         4605   NaN\n",
       "1   360576         1581   NaN\n",
       "2    98688         1964   NaN\n",
       "3    98688         3645   NaN\n",
       "4   295296         3361   NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf0df216-ec73-4ea7-af0b-49441e85eba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>376517</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>234512</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>344532</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>186135</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30230</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age_range  gender\n",
       "0   376517        6.0     1.0\n",
       "1   234512        5.0     0.0\n",
       "2   344532        5.0     0.0\n",
       "3   186135        5.0     0.0\n",
       "4    30230        5.0     0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1376a6c6-772f-441c-b9eb-8cb7a6d5f048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>action_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>328862</td>\n",
       "      <td>323294</td>\n",
       "      <td>833</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>328862</td>\n",
       "      <td>844400</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>328862</td>\n",
       "      <td>575153</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>328862</td>\n",
       "      <td>996875</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>328862</td>\n",
       "      <td>1086186</td>\n",
       "      <td>1271</td>\n",
       "      <td>1253</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type\n",
       "0   328862   323294     833       2882    2661.0         829            0\n",
       "1   328862   844400    1271       2882    2661.0         829            0\n",
       "2   328862   575153    1271       2882    2661.0         829            0\n",
       "3   328862   996875    1271       2882    2661.0         829            0\n",
       "4   328862  1086186    1271       1253    1049.0         829            0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "435b6834-6785-476c-ae38-da28df11dc6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0\n",
       "merchant_id    0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fac70ec6-cedf-45aa-ad14-836f92bf02c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id             0\n",
       "merchant_id         0\n",
       "prob           261477\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2297fc3-b4ad-4f95-9921-ab5fa988bc80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      0.000000\n",
       "age_range    0.005227\n",
       "gender       0.015173\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.isna().sum()/user_info.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02e94f27-800c-490f-8c35-4001fbda7c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0.000000\n",
       "item_id        0.000000\n",
       "cat_id         0.000000\n",
       "seller_id      0.000000\n",
       "brand_id       0.001657\n",
       "time_stamp     0.000000\n",
       "action_type    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_log.isna().sum()/user_log.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "128dcd35-1c1b-4d2d-9216-6b14fb78663e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正负样本数量:\n",
      " label\n",
      "0    244912\n",
      "1     15952\n",
      "Name: user_id, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_gp=train_data.groupby('label')['user_id'].count()\n",
    "print('正负样本数量:\\n',label_gp)\n",
    "_,axe = plt.subplots(1,2,figsize=(12,6))\n",
    "train_data.label.value_counts().plot(kind='pie', autopct='%1.1f%%', shadow=True, explode=[0,0.1], ax=axe[0])\n",
    "sns.countplot(x='label', data=train_data, ax=axe[1],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "920f484a-6835-426a-ae58-a8ad40ef3054",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选取top5店铺\n",
      "店铺\t购买次数\n",
      "merchant_id\n",
      "4044    3379\n",
      "3828    3254\n",
      "4173    2542\n",
      "1102    2483\n",
      "4976    1925\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('选取top5店铺\\n店铺\\t购买次数')\n",
    "print(train_data.merchant_id.value_counts().head(5))\n",
    "train_data_merchant = train_data.copy()\n",
    "train_data_merchant['TOP5'] = train_data_merchant['merchant_id'].map(lambda x: 1 if x in [4044,3828,4173,1102,4976] else 0)\n",
    "train_data_merchant = train_data_merchant[train_data_merchant['TOP5']==1]\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title('Merchant VS Label')\n",
    "ax = sns.countplot(x='merchant_id', hue='label', data=train_data_merchant)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74e0307b-fa13-4eba-af7b-57b3b2feae3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_repeat_buy = [rate for rate in train_data.groupby(['user_id'])['label'].mean() if rate <=1 and rate > 0]\n",
    "plt.figure(figsize=(8,6))\n",
    "ax=plt.subplot(1,2,1)\n",
    "#直方图\n",
    "sns.distplot(user_repeat_buy,fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "#QQ图\n",
    "res = stats.probplot(user_repeat_buy,plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d4ea547-3bf7-45ea-bd21-f4b2b4d63e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data_user_info = train_data.merge(user_info,on=['user_id'],how='left')\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Gender VS Label')\n",
    "ax = sns.countplot(x='gender', hue='label', data=train_data_user_info)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5bf3f1d0-f7d0-4709-9def-3625d68832da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['gender'])['label'].mean()]\n",
    "plt.figure(figsize=(8,4))\n",
    "ax=plt.subplot(1,2,1)\n",
    "#直方图\n",
    "sns.distplot(repeat_buy,fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "#QQ图\n",
    "res = stats.probplot(repeat_buy,plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "18744782-d39e-45fc-8dcb-63a3b12b375f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Avg VS Label')\n",
    "ax = sns.countplot(x='age_range',hue='label',data=train_data_user_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c0903096-c55b-4a4e-a6c9-5222986ffb0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqkAAAGDCAYAAAAI+YkKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB6j0lEQVR4nO3dd1yVdf/H8ddhizLFgYiKe5uZpnZrOVLLHJm/UnOWpuXILO+ybN+pZZk2zXJnWnlbWmbLbWqKAyX3RlyoCCh4WNfvjyvPLYnIOHAO8H4+HucB1/qez7k4HD58p8UwDAMRERERESfi4ugARERERET+SUmqiIiIiDgdJakiIiIi4nSUpIqIiIiI01GSKiIiIiJOR0mqiIiIiDgdJakiIiIi4nSUpIqIiIiI01GSKiIiIiJOR0mqiIhILhw7dgyLxWJ7+Pj48K9//YuNGzfa/TnCw8NzXcZrr71G/fr1s3XuwIEDGThw4E2Pr1mzBovFkmUZFouFNWvW5CBC+8jJz+PauceOHSvwOCX7lKSKiIjkwVtvvcXWrVv59ttvKVu2LO3atePo0aOODitXXnvtNV577bU8lbF161aaNGmS6bGdO3cyderUPJV/KwXx87h06RKvvfYaly5dsmu5kpGSVBERkTyoWrUqd9xxB506dWLRokX4+Pgwc+ZMR4eVK1WqVKFKlSp5KuOOO+7Ax8cn02MFkaQWxM/j0qVLvP7660pS85mSVBERETvx8PCgatWqhbYmtajRz6NwU5IqIiJiR2fOnCE4ONi2XaVKFebMmcPGjRtp27YttWvXznD+2bNn6dWrFz4+PpQpU4ZnnnkGq9Wa4ZzNmzdTt25dvLy86NSpE1FRURmOL1iwgLp16+Lt7U3t2rVZuHDhDXG98cYbBAQEEBQUxCuvvIJhGDecc6s+qdmRWZ/UgQMHYrFYGDRoEMePH7f1G73WteDkyZO4urqyePHiDNfVqFGD0aNH5ymef/48biWrn8ecOXOwWCyEhYUBEBYWhsVi4Z577slTjJI5N0cHICIiUhRcuHCBTz/9lBMnTvB///d/GY5t2rSJ5557jkGDBtG/f3/b/qtXr9KuXTvS09NZsGABsbGxPPfcc0RFRWVI2F588UXeffddgoODGTNmDA8//DCbNm0C4I8//qBfv36MGTOG7t27s3r1avr378+dd95J1apVAdi/fz8//PADixYtIiIighdffJEKFSowbNiwArgzZl/XESNG8OOPPzJjxgyWLVsGQIUKFQCoWLEibdq0YeHChfTs2ROA7du3c+jQIQYMGJCr58zq53Ezt/p5dOnSha1bt3L69Gm6du3KsmXLCA4Ovmn3BskjQ0RERHLs6NGjBpDh4evra3zxxRcZzqtcubLh4eFhbNq06YYyZs+ebbi4uBj79u2z7fvmm28MwNi5c6ftOV5++WXb8V9//dUAbOVt27bNmDFjhpGammoYhmGcPXvWcHd3N77++mvDMAzj1VdfNSwWi3Hw4EFbGX369DFq1ap1QzwDBgwwBgwYcNPXvHr1auNWqQNgrF69OtNjs2fPNipXrpzpsTlz5hienp7GpUuXDMMwjH//+99GgwYNsnyu62X353H9uUePHr0hvqx+Hre6XuxLzf0iIiJ5MGnSJHbs2MGhQ4eIjY3l8ccfv+Gcxx57jObNm9+wf+vWrVSsWJFatWrZ9rVr18527Jq7777b9n2zZs0AOHjwIAC33347devW5bnnnqNZs2aEhoaSmppKYmKi7ZqQkBCqV6+eoYwjR46QlpaW25dtdw899BCurq589913AHzzzTcZap2zKzs/j5vJ7s9DCoaSVBERkTyoXLkyt912G9WqVcPFJfM/q3feeWem+w3DuGHe0WtlGNf1Gb2+XFdXVwBbgvnJJ5/Qpk0bEhMTGTVqFHv37qVSpUqZlnl9GYZhZNov1VFKlSpF9+7dWbhwIVu2bCEqKopHH300x+Vk5+dxM9n9eUjBUJIqIiLiIE2bNiUqKspWKwqwcuVK27Fr/vjjD9v31yb2v1Yz+vnnn9OrVy8+++wz+vbti4+PDxcvXszwPCdPnuTEiRMZyqhcuTJubgU7NMXLy4ukpKSbHu/Xrx8rV67kgw8+oEOHDjka8GQP2f15eHl5AWT5WiTvlKSKiIg4SO/evalduzYPPvggP/zwA/PmzePJJ5+kR48e3HbbbbbzJk+ezKxZs/jxxx956qmnaNy4MXfddRcAQUFBbN68mZUrV7JgwQJatWpFQkICqampGZ6rV69e/Pbbb0yZMoWvvvqKESNGFORLBaBJkyZcuHCBGTNmsGbNGt5++23S09Ntx++9917KlCnDggULctXUn1fZ/XmUL1+eSpUq8cEHH7BhwwZmzZrFnj17Cjzeok5JqoiIiIN4eXmxatUq6tWrR+/evRkzZgx9+vRhwYIFGc57+eWXmThxIr1796Zq1aosW7bM1iz94YcfUq5cObp27cqbb77J4MGDueOOO9iwYYPt+jp16tCqVSt69uzJlClTGD9+PKNGjSrQ1wrmlFIzZszgP//5D/feey/z58/PcNzV1ZVevXrh5+dH9+7dCzy+7P48AL7++ms2b95M27ZteeWVV0hOTi7weIs6i6FOFiIiIuJgERERXL58mRdffJE6deowffp0R4ckDqaaVBEREXG4VatW0aZNG6xWK6+//rqjwxEnoJpUEREREXE6qkkVEREREaejJFVEREREnI6SVBERERFxOkpSRURERMTpFOxSE/koPT2dU6dO4ePjc8OSZiIi9mAYBgkJCVSoUCHHyy0WBvocFZH8lpPP0SKTpJ46dYrQ0FBHhyEixUBUVBQVK1Z0dBh2p89RESko2fkcLTJJqo+PD2C+aF9fXwdHIyJFUXx8PKGhobbPm6JGn6Mikt9y8jlaZJLUa01Tvr6++nAVkXxVVJvC9TkqIgUlO5+jRa9TlYiIiIgUekpSRURERMTpKEkVEREREaejJFVEREREnI6SVBERERFxOkpSRURERMTpKEkVEREREaejJFVEREREnI6SVBERERFxOkpSRURERCTnEhPh9dfNr/mgyCyLKiIiIiIFZNcu6N0b9uyB06dh+nS7P4WSVJFC5Ks/T+Tquj53VrJzJCIiUiwZBnz8MTz3HFitUL489OyZL0/lkOb+kSNHYrFYbI/q1asDEBkZSdOmTQkICGDs2LEYhuGI8ERERETkn86fh27dYORIM0Ht3NmsUW3fPl+eziFJ6rZt21i+fDmxsbHExsayY8cOrFYrXbp0oUmTJoSHh7Nnzx7mzJnjiPBERERE5HqrVkGjRvDDD+DhAdOmmd+XKZNvT1ngSWpqaiqRkZG0bt0af39//P398fHxYcWKFcTFxTFlyhSqVavGhAkTmDlzZkGHJyIiIiLXpKTASy+ZtaWnTkHt2vDnnzBqFFgs+frUBZ6k7tq1C8MwuO222yhRogSdOnXixIkTRERE0Lx5c7y9vQFo2LAhe/bsuWk5VquV+Pj4DA8RERERsZMjR6BVK5gwweyLOngwhIfDbbcVyNMXeJK6d+9e6tWrx8KFC9mzZw/u7u4MHTqU+Ph4wsLCbOdZLBZcXV2JjY3NtJyJEyfi5+dne4SGhhbUSxAREREp2hYuNJPRP/8Ef3/49lv4/HMoWbLAQijwJPXRRx9l8+bNNG3alLCwMD766CN+/fVX0tPT8fT0zHCul5cXiTeZe2vcuHHExcXZHlFRUQURvoiIiEjRdfkyDBoEffpAQgLcdRfs3JlvI/iz4vApqPz9/UlPT6d8+fJERkZmOJaQkICHh0em13l6et6Q1IqIiIhILm3bZs59evAguLjA+PHw8svg5ph0scBrUseMGcM333xj2966dSsuLi40aNCAzZs32/YfO3YMq9VKYGBgQYcoIlJgcjP13tq1a6lTpw5BQUFMmTIlw7HJkydTrlw5fH19eeihh7hw4UJ+hS4iRUV6Orz3HrRoYSaooaGwZo25mpSDElRwQJJ622238dJLL7Fu3TpWrVrFyJEjGThwIB06dCAuLo558+YBMGnSJNq3b4+rq2tBhygiUiByM/VeTEwMXbt2pXfv3mzatIkFCxawevVqANatW8fcuXNZt24d27dv5+rVqzz77LMF8EpEpNA6cwbuu8+cnD8lBXr0MJv3W7VydGQF39zfv39/9u7dS7du3fDx8eHBBx9kwoQJuLm5MWPGDPr06cPYsWNJS0tj7dq1BR2eiEiBuX7qPW9vbyZMmMDw4cMZNGjQTa9ZsGABwcHBvPzyy1gsFl555RVmzpxJmzZt2LJlC/fffz+1atUCoHfv3nzyyScF9XJEpLD5+WcYMADOnYMSJeD99+GJJ/J9aqnscshk/hMnTiQ2NpYTJ04wbdo0Sv49Uqx79+4cPHiQGTNm2GYBEBEpqnI69d61a9q2bYvl7z8izZo1Y/v27QDUr1+fJUuWcPjwYc6dO8fMmTO59957b1qWpvITKaasVnj2WbMG9dw5aNDAnFpq6FCnSVDBQUlqVkJCQujWrRtl8nEFAxERZ5DTqfcyu8bX15fo6GgAOnXqRI0aNahevTrlypXjypUrvPDCCzctS1P5iRRDBw6YfU+v9WcfMQK2bIG6dR0bVyacLkkVESku3NzccjT1XmbXXH/+N998w/Hjx9m3bx8XLlygfv369O3b96ZlaSo/kWLEMGDOHLj9dtixA0qXhqVL4cMPwcvL0dFlyuFTUImIFFeBgYE5mnrv2jUxMTGZnr9w4UKefPJJW5/UqVOn4ufnx6VLl/D397+hLE3lJ1JMxMXBsGGwaJG53aYNzJ8PISGOjesWVJMqIuIgTZs2zfHUe/+8ZufOnYT8/YcmNTWVs2fP2o6dPn0agLS0NHuHLiKFxaZN5spRixaBq6u5xOlvvzl9ggpKUkVEHKZ169Y3nXovPj6elJSUG67p2rUrGzZsYPXq1aSmpvLuu+/SsWNHAO666y5mzJjB9OnTmTt3Lr169aJFixaULl26QF+XiDiBtDR46y1zKqljxyAsDDZsgHHjzGS1EFBzv4iIg2Q19V7Dhg2ZOnUq3bt3z3BNUFAQ7733Hh07dsTPz4+SJUsyc+ZMAEaPHs2pU6d48803OX/+PC1atLAdE5FiJDoa+vY1J+QHcxWpTz8FPz+HhpVTFiM7y5sUAvHx8fj5+REXF4evr6+jwxHJF1/9eSJX1/W5s5KdIyme8utzJjo6mvDwcFq2bJntmU0OHTrE3r17ufvuu+0Wiz5HRYqApUvhscfg4kUoWRI+/hj693eaqaVy8jmjmlQREQcLCQmx9SvNrurVq1O9evV8ikhECp2kJHPVqGsLeNx+OyxcCDVrOjauPFCfVBEREZHC7K+/oFmz/yWozz5rDpgqxAkqqCZVREREpHAyDPjsM3jmGbh6FcqVg7lz4e/BlIWdklQRERGRwubiRXj8cfj+e3O7Uydzsv5y5RwZlV2puV9ERESkMFm7Fho1MhNUd3dzidPly4tUggqqSRUREREpHFJT4Y03zPlP09PNPqcLF5qDpIogJakiIiIizu7YMXj0Udi40dweNAg++ABKlXJoWPlJzf0iIiIizuybb8ylTTduBF9fs/Z01qwinaCCalJFREREnNOVK/D003Bt5bjmzeGrr8wlTosB1aSKiIiIOJsdO6BJEzNBtVjgpZdg3bpik6CCalJFREREnIdhwLRp8PzzkJwMISEwfz60aePoyAqcklQRERERZ3DunDkg6qefzO1u3cya1NKlHRuXg6i5X0RERMTRfvvNnPv0p5/A0xM+/hi++67YJqigJFVERETEcZKTzab9Dh3gzBmoVw+2boWnnjL7ohZjau4XERERcYRDh6B3bwgPN7effBLeew9KlHBsXE5CNakiIiIiBW3+fGjc2ExQAwJgyRL45BMlqNdRTaqIiIhIQYmPN5vyFywwt1u3hi+/hNBQx8blhFSTKiIiIlIQtmwxa08XLABXV3jzTVi1SgnqTagmVURERCQ/pafD5MkwfjykpkLlyubKUS1bOjoyp6YkVURERCS/nDoF/fvDypXm9sMPw2efgb+/Q8MqDNTcLyIiIpIffvzRnPt05Urw9jYn5l+0SAlqNqkmVURERMSerl415z794ANz+7bbzOS0Vi2HhmVPaWmwfj2cPg3BwdCqldnN1p6UpIqIiIjYy9695tynERHm9ujRMGmSuYpUEbFkCTz9NJw8+b99FSvCtGnQo4f9nkfN/SIiIiJ5ZRjw+efQpImZoJYpA8uXw/vvZ5mgpqXBmjWwcKH5NS2twCLOlSVLoGfPjAkqQHS0uX/JEvs9l5JUERERkbyIjTUHRD3xBCQlwb33monq/fdnedmSJVClCrRpA336mF+rVLFvomdPaWlmDaph3Hjs2r7Ro+2XaCtJFREREcmtDRvMPqeLF4ObmznV1M8/mx01s1CQNZL2sn79jfFezzAgKso8zx6UpIqIiIjkVGoqvP463H03nDgB1arBxo3w3HPgknV6VdA1kvZy+rR9z7sVDZwSERGRYitXo9RPnIC+ff9XZdi/P3z0Efj4ZOs5c1Ijec892SqyQNyicjjH592KalJFRESkWMpVn9D//tec+3T9ejMp/fJLmDs32wkqFHyNpL20amWO4rdYMj9usZgrvLZqZZ/nU5IqIiIixU6O+4QmJsLQoebBS5egWTPYsQMefTTHz13QNZL24upqTjMFNyaq17anTrXffKlKUkVERMTp5OfUTDnuExoRAXfcATNmmNnYCy+YA6aqVcvV8xd0jaQ99ehhjhELCcm4v2JFc78950lVn1QRERFxKvk9WXy2+4SuM7gn8iMYOxasVrNqc/58aNcuT89/rUayZ08zIb0+Wc6PGkl769EDunXL/xWnVJMqIiIiTqMgpmbKTl/P0pyn+nPdYNQoM0Ht3NmsUc1jgnpNQdZI5gdXV3NQV+/e5tf8SKiVpIqIiIhTKKipmW7V17MtK9lFQypu/wE8POCDD+CHH8xVpOyoRw84dgxWr4avvjK/Hj3q/AlqQVFzv4iIiDiFgpqa6Vqf0OjojAmxGym8wSs8z9u4YGDUro1l0SJzNH8+uVYjKTdSTaqIiIg4hYKamimzUephHGED/2Ick3DB4Oi9T2DZti1fE1TJmpJUERERcQoFOTXT9X1Ce/MVO7mNO9lCnMWfzc99S9ivn4G3d96fSHJNSaqIiIg4hYKemqnHvQkcbzOAr3gUXxK41OBflDocQfPJPe3zBJInSlJFRETEKRToZPHh4XD77bjMnwcuLvDqq/hvX41rWCU7FC72oCRVREREnEa+T82Ung6TJ0PLlnDokFk1u2YNvPYauGk8uTNxeJLaqVMn5syZA0BkZCRNmzYlICCAsWPHYmQ2B4WIiIgUafk2NdOZM9CpE/z735CSAg89ZM596oxLO4ljk9QFCxbwyy+/AGC1WunSpQtNmjQhPDycPXv22JJXERERKV7sPln8ihXQsCH89huUKGEucfrttxAQYIdoJT84LEm9ePEizz77LLVq1QJgxYoVxMXFMWXKFKpVq8aECROYOXOmo8ITERGRosBqhWeegfvvh5gYM1END4chQ24+QkucgsM6Xzz77LM8+OCDJCUlARAREUHz5s3x/nu6h4YNG7Jnz56bXm+1WrFarbbt+Pj4/A1YRERECpf9+6FXL9i509weNQrefhu8vBwalmSPQ2pSV69ezcqVK3n77bdt++Lj4wkLC7NtWywWXF1diY2NzbSMiRMn4ufnZ3uEhobme9wiIiJSCBgGzJoFt99uJqilS8OyZebUAUpQC40CT1KvXr3K0KFD+fTTT/H19bXtd3Nzw9PTM8O5Xl5eJCYmZlrOuHHjiIuLsz2ioqLyNW4REREpBC5dMmtPH38cEhOhXTvYtQu6dHF0ZJJDBd7c/+abb9K0aVM6d+6cYX9gYCCRkZEZ9iUkJODh4ZFpOZ6enjcktSIiIlKMbdwIffrA8ePmdFL/+Q+MHWvOgyqFToEnqV999RUxMTH4+/sDkJiYyDfffEOVKlVISUmxnXfs2DGsViuBgYEFHaKIiIgUJmlpMHGiOddpWhpUrQoLF0KzZo6OTPKgwJPU9evXk5qaatt+7rnnaN68OQMHDqRu3brMmzeP/v37M2nSJNq3b4+rXZaVEBERkSLp5Eno2xfWrjW3H30UPvkErutSKIVTgSepFStWzLBdqlQpgoKCCAoKYsaMGfTp04exY8eSlpbG2mtvOBEREZF/+v57s+/pxYtQqpSZnPbr5+ioxE4cvv7X9RP2d+/enYMHDxIeHk7Lli0pU6aM4wITERER55SUBM8+C59+am7fcYfZvF+9umPjErtyeJL6TyEhIYT8c8FeEREREYDISHP0/l9/mdv//je8+SbcZKC1FF5Ol6SKiIiI3MAwzJrTZ5+Fq1ehXDmYNw86dHB0ZJJPlKSKiIhIjqWlwfr1cPo0BAdDq1aQb2OdL1ww+54uXWpu338/zJ4NZcvm0xOKM9DEYSIiIpIjS5ZAlSrQpo05LWmbNub2kiX58GRr1kCjRmaC6uEBU6fCjz8qQS0GVJMqUsC++vOEo0MQEcm1JUugZ0+z9f160dHm/sWLoUcPOzxRSgq8/jpMmGA+Wa1a5uCoxo3tULgUBqpJFRFxoMjISJo2bUpAQABjx47F+Odf/kysXbuWOnXqEBQUxJQpUzI9p1evXowcOdLe4Uoxl5YGTz99Y4IK/9s3erR5Xp4cOwZ33w1vvWUW/PjjsG2bEtRiRkmqiIiDWK1WunTpQpMmTQgPD2fPnj0ZpuXLTExMDF27dqV3795s2rSJBQsWsHr16gzn/PLLL6xatYo333wzH6OX4mj9enPu/JsxDIiKMs/LtUWLzOb9TZvAzw++/hq++AJKlsxDoVIYKUkVEXGQFStWEBcXx5QpU6hWrRoTJkxg5syZWV6zYMECgoODefnll6lRowavvPJKhmuSkpJ46qmnmDRpkm35aSme0tLM7pwLF5pf81y7iTlIyp7nZXD5Mjz2GPTuDfHx0LIl7NwJDz+ci8KkKFCSKiLiIBERETRv3hxvb28AGjZsyJ49e255Tdu2bbFYLAA0a9aM7du3246/+eabJCUl4ebmxqpVq7LsPmC1WomPj8/wkPyXH8njP+XXwKbgYPueZ7N9OzRpYo7Yd3GBV14xlzmtUiWnIUoRoiRVRMRB4uPjCQsLs21bLBZcXV2JjY3N9jW+vr5ER0cDcOLECaZMmUL16tU5ceIEY8eOpUePHjdNVCdOnIifn5/tERoaaqdXJjdTEKPirw1s+mez/LWBTXl5rlatoGJF+Pt/pBtYLBAaap6XLenpMGUKNG8OBw6Yha9aZQ6YctPY7uJOSaqIiIO4ubnh6emZYZ+XlxeJiYnZvub68+fMmUO5cuX47bffGD9+PGvWrGHt2rX89ttvmZY1btw44uLibI+oqCg7vCq5mfxMHq/J74FNrq4wbZr5/T8T1WvbU6dmc77Us2ehc2dzcv6UFHjwQYiIMAdMiaAkVUTEYQIDA4mJicmwLyEhAY8slnf85zXXn3/y5EnatWtnS2J9fHyoUaMGR48ezbQsT09PfH19MzwkfxTUqPiCGNjUo4c5zdQ/VzCvWDEH00/9+qs5OOrnn8HLC6ZPh//+FwIDcx+YFDlKUkVEHKRp06Zs3rzZtn3s2DGsViuBWfyh/uc1O3fuJOTvbCE0NJSkpCTbsfT0dE6ePEnlypXzIXrJiQIZFU8+D2y6To8e5ixRq1fDV1+ZX48ezUaCmpwMzz0HHTuaNan160N4OAwdevM+BFJsKUkVEXGQ1q1bExcXx7x58wCYNGkS7du3x9XVlfj4eFJSUm64pmvXrmzYsIHVq1eTmprKu+++S8eOHQF4+OGH+eGHH/jvf//LyZMnGTduHFarlbvuuqtAX5fcqKCSx3wb2JQJV1e45x5zMP4992Sjif/gQXPE/nvvmdvDh8OWLVCvXt6DkSJJvZJFRBzEzc2NGTNm0KdPH8aOHUtaWhpr164FzJH+U6dOpXv37hmuCQoK4r333qNjx474+flRsmRJ2xRUtWrV4uuvv2b8+PHs27ePatWqsXTpUnx8fAr6pck/FFTyeG1gU3R05l0LLBbzeLYHNtmDYcC8eWZSeuWK2aQ/axZ061aAQUhhZDGys7xJIRAfH4+fnx9xcXHqVyVOzRHLova5s1KBP2dRlF+fM9HR0YSHh9OyZUvKlCmTrWsOHTrE3r17ufvuu+0Wiz5H809amjmK/1bJ49Gj2Rx0lIVrA7Qg43Nda02327Kl2REXB08+ac63BeZ0BvPn39ihVYqNnHzOqLlfRMTBQkJC6NatW7YTVIDq1avTpUsXJZOFhF1Hxd+CXQY22cPmzeYypgsXmi/srbfgt9+UoEq2KUkVEREpAAWZPOZ6YJM9pKXBxInwr3+ZT1qlCmzYAC++aJ8sXIoN9UkVEREpID16mF0x1683B0kFB5v9Q/Mjd7s2sKlARUdDv35mVgzmqKpPPwU/vwIORIoCJakiIiIFyCHJY0H44QcYNAguXICSJeHjj6F/f00tJbmm5n4RERHJvatXYeRI6NrVTFBvvx22b4cBA5SgSp4oSRUREZHc2bMHmjWDjz4yt599FjZuhJo1HRuXFAlq7hcREZGcMQyYMcNcy/XqVShb1pwL9e+FJUTsQTWpIiJ5cODAAdv33333Hf/9738pItNPi2Tu4kVzItZhw8wEtWNH2LVLCarYnZJUEZFceuedd7jjjjswDIN///vfDB8+nBEjRjB8+HBHhyaSP9atg0aNzBUD3N3NJU5/+gnKlXN0ZFIEKUkVEcmlKVOmsGrVKiwWC/Pnz2fdunWsXLmSRYsWOTo0EftKTYVXXzVXjDp5EmrUMCfrHzMGXJRKSP7QO0tEJJesViuBgYHs3bsXb29vqlevjsViwaIRzVKUHD9uzpn1xhuQnm5OM7V9uzmKXyQfaeCUiEgudenShfvuu4/09HQeeeQRjh07xlNPPUWHDh0cHZqIfXz7LQwZAnFx4OsLn30GvXo5OiopJpSkiojk0hdffMHs2bMpUaIEffr0Yd++fbRs2ZLnn3/e0aGJ5M2VK+bI/S++MLebNzfXVw0Lc2hYUrwoSRURySUPDw+GDh1q265fvz5vvfWWAyMSsYOdO83lTPftMyfjf/FFsz+qu7ujI5NiRn1SRUTyYOPGjbzwwgv06dOH48eP8+9//5urV686OiyRnDMMmDYN7rzTTFArVICVK+E//1GCKg6hJFVEJJc++OADOnXqxIkTJ/j+++9JTk5my5YtmoJKCp9z5+CBB8wm/uRkc4nTiAhzNL+IgyhJFRHJpbfffpvffvuNr776Ck9PTzw9Pfnkk0/47rvvHB2aSPb99ps59+lPP4GnJ3z8MXz/PQQFOToyKeaUpIqI5JK7u/sN002lp6fj4+PjoIhEciA5GZ5/Hjp0gDNnoF492LoVnnrK7Isq4mAaOCUikkvDhw/n/vvv54knniAlJYXFixezcOFCRo4c6ejQRLJ26JA5OCo83NweNsxcPcrb27FxiVxHSaqISC6NHTuW8uXLM2/ePCpVqsTPP//M6NGjefTRRx0dmsjNzZ9v1pZevgwBATBzJjz4oKOjErmBklQRkTzo168f/fr1c3QYIrcWH28mpwsWmNutW8OXX0JoqGPjErkJJakiIrnUpk2bmy6BumrVqgKORiQLW7aYzftHjoCrK7z2GowbZ34v4qSUpIqI5NLAgQNt3ycmJrJ161a+//57Xn/9dccFJXK99HR45x14+WVITYVKlcyVo+66y9GRidySklQRkVwaMGBAhu0nn3ySIUOGMHHiRA2ecnJpabB+PZw+DcHB0KpVEaxUPHUK+vc3J+QHePhh+Owz8Pd3aFgi2aUpqERE7KhFixbs2bPH0WFIFpYsgSpVzHnq+/Qxv1apYu4vMn780Zz7dOVKc8T+zJmwaJESVClUVJMqIpJLb7zxRobttLQ0Nm7cSJkyZRwUkdzKkiXQs6e5Auj1oqPN/YsXQ48ejonNLq5eNec+/eADc/u222DhQqhd26FhieSGklQRkVw6evRohm2LxUKLFi148sknHRSRZCUtDZ5++sYEFcx9Fou5Kmi3boW06X/vXnNwVESEuT16NEyaZK4iJVIIKUkVEcml2bNnOzoEyYH16+HkyZsfNwyIijLPu+eeAgsr7wwDvvjCzMCTkqBMGZgzB+6/39GRieSJklQRESkWTp+273lOITYWnnjC7KcA0L49zJtnjgYTKeQ0cEpERIqF7OZthSa/++MPs8/p4sXg5mZONfXLL4XoBYhkzWE1qRcuXGD//v3UrFmToKAgR4UhIpJjw4cPx93dPctzZs2aVUDRSHa1agUVK5qDpDLrl2qxmMdbtSr42HIkNRXeegveeMOcB7VaNXNwVNOmjo5MxK4cUpO6aNEiqlevzvDhw6lUqRKLFi0CIDIykqZNmxIQEMDYsWMxMvsUERFxsNDQUCpXrpzlQ5yPqytMm2Z+/8+Fwq5tT53q5IOmTpyAtm3NFaPS0815UHfsUIIqRVKB16ReunSJkSNHsn79eurXr8/8+fN5/vnnefDBB+nSpQsdO3Zk0aJFjBo1ijlz5jBo0KCCDlFEJEsvvPACvr6+jg5DcqFHD7N1/OmnMw6iqljRTFCdevqp//4XBg+GS5fAxwc+/RQefdTRUYnkm1zVpFasWJExY8bw559/5vjahIQEpk6dSv369QFo1KgRsbGxrFixgri4OKZMmUK1atWYMGECM2fOvGk5VquV+Pj4DA8REZFb6dEDjh2D1avNFUJXr4ajR504QU1MhGHDzIlcL12CZs3M2lMlqFLE5SpJnT17NsnJyfzf//0fYWFhPP/882zfvj1b14aGhvLo379YKSkpvPvuu/To0YOIiAiaN2+Ot7c3AA0bNsxy1ZaJEyfi5+dne4SGhubmpYiISDHk6mpOM9W7t/nVaZv4d+0ym/I/+8zsk/DCC7Bhg9kPVaSIy1Vz/7333su9997LRx99xNatW/n+++9p3749QUFB9OrVi1GjRt1yMFRERARt2rTBw8ODffv28eabbxIWFmY7brFYcHV1JTY2loCAgBuuHzduHGPGjLFtx8fHK1EVkQJlGAaLFy/mwIEDpKWl2fbt2LGD77//3rHBSeFmGPDxx/Dcc2C1miP258+Hdu0cHZlIgclTn9TNmzezZMkS/vvf/+Lh4cG9997L2bNn6dixI9u2bcvy2oYNG7Jy5Uqee+45Bg0aRM2aNfH8x6oYXl5eJCYmZpqkenp63nC+iEhBGjJkCGvWrMHV1ZWSJUtSo0YNlixZQu/evR0dmhRm58/DY4/BDz+Y2507w+zZ5iT9IsVIrpr7R44cSWhoKN26dSMuLo7PP/+cU6dO8fHHH/PSSy+xb9++W5ZhsVho3Lgxc+bMYenSpQQGBhITE5PhnISEBDw8PHIToohIvluyZAm//vor7777LhUqVODrr7/m/fff5+rVq44OTQqrVaugUSMzQfXwgA8+ML9XgirFUK6S1KSkJGbOnMmpU6f47LPPaNu2LS4uZlFly5bl4MGDN7121apVjB071rbt5mZW5tauXZvNmzfb9h87dgyr1UpgYGBuQhQRyXceHh5cvnyZli1bsmXLFgB69uzJb7/95uDIpNBJSYEXXzRXjDp1CmrXhi1bYOTIG+fLEikmctXc/8UXX9z0mJeXFxUqVLjp8dq1a9O9e3dq1KjBfffdx/jx4+nQoQOdO3dmyJAhzJs3j/79+zNp0iTat2+Pq9P2ZheR4m7o0KG0bt2a48ePU69ePXr37k1aWpr6x0vOHDkCffrAtRlznngC3n8f/h5ILFJc5aom9ZtvvrENErhm/fr19OvX75bXVqhQgW+//ZapU6dSr149EhMTmT9/Pm5ubsyYMYNhw4ZRrlw5Fi9ezKRJk3ITnohIgXj99ddZsGABHh4ezJ07Fy8vLwzD4Msvv3R0aFJYLFxoLm3655/g7w/ffmuO5FeCKoLFyMWyTtdG3V8/mfWpU6eoUaMGV65cyVNA0dHRhIeH07JlS8rkoA9OfHw8fn5+xMXFaZJtcWpf/XmiwJ+zz52VCvw5i6Ki/jlT1F+fU7l82WzKnzPH3P7Xv2DBAqik31Up2nLyOZOj5v4TJ8w/roZhEBUVhY+Pj237xx9/zLKZP7tCQkIICQnJczkiIvnlzjvvpFOnTtx7773cc889tvmdRbJl2zbo1QsOHQIXF3jlFXjpJXAr8EUgRZxajn4jqlSpgsViwWKx0KBBA9t+i8VCjRo1mDFjht0DFBFxNo8//jhbtmxh8ODBXLx4kZYtW3LvvffSoUMHmjRp4ujwxFmlp5t9TceNMwdKhYaataetWjk6MhGnlKMkNT09HQAXFxdiY2Px8/PLl6BERJzZE088wXPPPQfAgQMHWL9+PevXr+eLL74gPj6e9u3bs3DhQgdHKU7lzBkYMAB+/dXcfugh+PxzyGQecBEx5WrgVK1atWxTR4mIFGclSpTA09MTd3d3XFxcSE1NJTk52dFhiTNZscKc+/TXX6FECZgxwxwgpQRVJEu5yjT37t1r7zhERAqNefPmsXXrVtatW0d0dDTNmjXj3nvvZd68eTRr1kxT54nJajWb9t9/39xu0AAWLYK6dR0bl0ghoepQEZEcmjp1Kh07duT999+nbdu2GgkvN9q/H3r3hh07zO2RI+Gdd8DLy7FxiRQiSlJFRHJo+/btSkwlc4ZhTis1YgQkJkLp0jB7NnTp4ujIRAqdbCepbdu25ccff8Tb25s2bdpguckybatWrbJbcCIiIoXGpUswbBh8/bW53bYtzJ8PdpieUaQ4ynaSOmDAADw8PAAYOHBgfsUjIiJS+GzcaC5tevy4Od/pm2/C2LGg/skiuZajJDWz70VERIqttDSYOBFee838PizMXOr0zjsdHZlIoac+qSIiIrlx8iT07Qtr15rbffrAp5+C+iuL2EWu5kkVEREp1r7/3pz7dO1aKFUK5s6FL79UgipiR7lKUi9evMhLL70EwOHDh+nWrRtdunTR/KkiIlK0JSXBU0/Bgw/CxYvQpAls3w79+8NNBhSLSO7kKknt27cvkZGRAIwYMQI/Pz9Kly7N448/btfgREREnEZkJDRtajbpgzkwauNGqFHDsXGJFFG56pO6bt069u7dy9WrV9mwYQPnzp3j0qVLVK9e3d7xiYg4neHDh+Pu7p7lObNmzcpWWZGRkQwaNIhDhw4xePBg3nnnnZtO8XfN2rVrGTZsGDExMbz44ouMGTPmhnNSUlK4/fbb+fDDD7nnnnuyFYvchGGYiemzz8LVq1CuHMybBx06ODoykSItVzWpZcqUYfPmzXz77bc0atSIEiVKsGvXLsqVK2fv+EREnE5oaCiVK1fGMAy++uorEhMTKV++PMnJySxatAir1ZqtcqxWK126dKFJkyaEh4ezZ88e5syZk+U1MTExdO3ald69e7Np0yYWLFjA6tWrbzjvnXfesbV4SR5cuGA27Q8fbiao990Hu3YpQRUpALmqSX3zzTd59NFH8fDw4Ntvv2XTpk08+OCDTJkyxd7xiYg4nRdeeAFfX1+aNGnC4sWLeeCBB2zHfv75Z15++eVslbNixQri4uKYMmUK3t7eTJgwgeHDhzNo0KCbXrNgwQKCg4N5+eWXsVgsvPLKK8ycOZM2bdrYzjl48CDvvvsuVapUyfVrFGDNGnP0fnQ0uLuby5qOGgUuGnMsUhBylaT27duXBx98EFdXV7y8vLh48SI7d+6kZs2a9o5PRMRpHT169IZEsGLFihw5ciRb10dERNC8eXO8vb0BaNiwIXv27LnlNW3btrV1CWjWrBnjxo3LcM7QoUN54YUXWLFiRZZlWa3WDLW+8fHx2Yq7yEtJgddfhwkTzKb+WrXMuU8bN3Z0ZCLFSq7nSS1ZsqTt+8DAQAIDA+0SkIhIYdG3b1+6devGiBEjCA0N5dSpU3z88cf07t07W9fHx8cTFhZm27ZYLLi6uhIbG0tAQMBNr6lbt65t29fXl+joaNv27NmziYuL49lnn71lkjpx4kRef/31bMVabBw7Zs53ummTuf344zBtGlz3N09ECkauktQlS5YwZswYoqKibPsMw8BisZCWlma34EREnNmUKVOoXr0633//PadPn6Zs2bIMHTqUkSNHZut6Nzc3PD09M+zz8vIiMTHxpknqP6+5dj6Y/VXHjRvHzz//jJvbrT/ex40bl2HQVXx8PKGhodmKvUhatAiGDoX4eHO+0xkz4JFHHB2VSLGVqyT1ySefZODAgQwePBgPDw97xyQiUii4ubkxatQoRo0alavrAwMDbxjclJCQkOXnamBgIDExMZmeP3r0aB5//HFuu+22bD2/p6fnDUlysXT5stnXdPZsc7tFC/jqK1CfXhGHylXvb4vFwhNPPEGNGjWoXLlyhoeISHHy1Vdf0atXL+666y4OHjzIww8/zPnz57N1bdOmTdm8ebNt+9ixY1it1iy7T/3zmp07dxISEmKL5cMPP8Tf3x9/f382bNjAAw88wKRJk3L56oqB7dvNCflnzzYn43/5ZVi3TgmqiBPIVZL6n//8h5EjR2b7g1hEpCh66aWXeOGFF6hatSoRERG4/D3qe+jQodm6vnXr1sTFxTFv3jwAJk2aRPv27XF1dSU+Pp6UlJQbrunatSsbNmxg9erVpKam8u6779KxY0fAHMi1a9cudu7cyc6dO7njjjv44osvGDZsmJ1ecRGSng7vvw/Nm8OBAxASAqtWwRtvQDa6SohI/svVb+KCBQv466+/qFy5MnXq1MH3urWKV61aZbfgRESc2eeff86aNWuoW7cun376Ke7u7kyYMIEmTZpk63o3NzdmzJhBnz59GDt2LGlpaaxduxYwR/pPnTqV7t27Z7gmKCiI9957j44dO+Ln50fJkiWZOXMmwA0zDXh5eVG+fHn8/f3z+lKLlrNnYeBA+Plnc7t7d/jiCyhd2pFRicg/5CpJHThwoJ3DEBEpfPz9/Tlx4kSG0fYXLlzI0cIm3bt35+DBg4SHh9OyZUvKlCkDmE3/N/PUU0/RoUMH9u7dy913352houB6a9asyXYcxcavv0L//mai6uUFU6bAsGFmU7+IOJVcJakDBgywfZ+cnGwbReqiCY5FpBgZP3483bt3p0ePHlitVqZOncrSpUt57bXXclROSEiIrV9pdlWvXl1LUedEcjK8+CK89565Xb++Ofdp/fqOjUtEbipXWWVCQgJPPPEE5cqVw9vbm8jISCpWrMi2bdvsHZ+IiNPq378/v/32GyVLluSee+7h8uXLzJ07l379+jk6NLnewYPQsuX/EtSnnoItW5Sgiji5XNWkDho0iMTERObNm8fDDz+Mn58fI0eOZPjw4RlGnYqIFHWtWrWiVatWjg5DMmMYMG8eDB8OV65AYCDMmgXdujk6MhHJhlzVpP7+++/MmDGDjh074uLigsVioV+/fvz111/2jk9ExGnt3r3bNpG+OJn4eOjb1xwgdeUK3HMPREQoQRUpRHKVpNauXZu5c+cC5pypFouFTZs2Ua9ePbsGJyLizDp27MjWrVsdHYb80+bNcNtt5oT8rq7wn//A779DxYqOjkxEciBXzf0fffQR9913H5988gkJCQk88sgjHD9+nGXLltk7PhERpzVw4EDmzp3L3Xff7ehQBCAtDd55x5yQPy3NnJD/q6/MFaREpNDJcZJ66dIlLl++zEcffURKSgqnT5+mfPnydO3aFT8/v/yIUUTEKbVr146XX36Zzp07M2LECEqWLGk71rp1awdGVgydOgX9+pkT8gM88gh89hno75JIoZXtJDUlJYXhw4fbmvmDgoKwWCycP38eFxcXdu/ezTvvvJNvgYqIOJvBgwcDcPr0aZ566inbfovFwpEjRxwVVvHzww8waBBcuADe3vDRR2ZfVM19KlKoZTtJHT9+PD/99BM//PAD7dq1w9XVFYD09HTWrFnDoEGDKF26NM8//3y+BSsi4kyOHj3q6BCKt6tXYexYMykFaNzYnPu0Vi3HxiUidpHtgVOLFi3i008/pUOHDrYEFcwJ/Nu2bcvHH3/M559/ni9BiogpLd3gyPnL/Hn0ApuOXODQucukpqc7OiyRgrdnDzRr9r8EdcwY2LRJCapIEZLtmtRTp07RIovO582aNeP48eN2CUpEMkpLN9hy7CKr953jsjU1w7GSHq78q0YZ/lU9CFcXNW8WpJSUFCZPnszSpUs5efIkv/76K4MGDWLhwoVUq1bN0eEVTYYBn38Oo0dDUhKULQtz50KnTo6OTETsLNtJalpaWoZm/n9KTU0lXTU6InZ32ZrKgj+Pc/yCOR+nt4crlQK9sVgsnLyYSII1lV/+OkNkdBx9m1fGr4S7gyMuPp566im2b9/O0KFDee655/D29qZ58+YMHTqU33//3dHhFT0XL8KQIbBkibndoYOZoJYv79i4RCRfZDtJnT17dn7GISKZuJSYzOfrjxCbmIKnmwsd65WnaZVAW41pWrrBzqhL/LT7NNGXkpi+9jCD/xVG6VKeDo68eFi8eDHbt28nLCyM559/HldXV8aMGUODBg0cHVrRs24dPPoonDwJ7u4wcSI88wy45Gq6bxEpBLKdpA4YMCA/4xCRf7hiTWXmhqPEJqYQWNKDAS2qUMYnY/Lp6mKhSeUAwoJKMmfjMc5ftjJ74zGG3V2NUp65mgZZciA0NJR169YRFhZm23fo0KEM25JHqanmZPxvvgnp6VC9OixaBE2aODoyEcln+hdUxAmlpRss2nqCC1eS8fd2Z/C/wm5IUK8XWNKDIa3CCPB25+KVZL768zhp6UYBRlw8vfPOOwwdOpSWLVuSmJjIc889R79+/Xj33XcdHVrRcPy4uZzp66+bCeqAAbB9uxJUkWJCSaqIE1q17xyHY67g4erCgBZV8Pf2uOU1Pl7uDGoZhqebC8cuJLJq39kCiLR469SpE3/99RedO3fm8ccf57bbbmPjxo106NDB0aEVft9+C40awR9/gI8PLFgAc+aY34tIsaD2QBEnczI2kbUHzgHw4O0hlPP1yva1QT6edG8cwtdbo1izP4a6wX6EBJTIr1AFqFatGi+99JKjwyg6rlwx+5pem9LwzjvNpU2rVnVsXCJS4JSkijiRtHSDxdtOkm5Aw4p+NKron+MyGlX0Z+/peHadjOO7HSd58p7q9g+0mGvYsCEutxiwoxWnciEiAnr1gn37zNWiXnjBbOp314wVIsWRmvtFnMimIxc4l2DF28OVrg0r5LqcBxpWoIS7K6firrL12EU7RigAn3zyCbNnz+aRRx7BYrHw73//m+nTpzNu3Djc3d3p1q2bo0MsXAwDPvjAnJx/3z4IDobff4cJE5SgihRjqkkVcRKXrams3Gv2I+1YrzzeeRidX8rTjXvrlmNZxCl+33uWuKQUzZ9qR//617/w9fXl0Ucf5ZdffqFevXq2Yy1btuSBBx7g/fffd2CEhUhMDAwaBMuXm9tdusCsWRAU5Ni4RMThVJMq4iTWHYjBmppOiH8JmlQOyHN5TasEUsbHk8TkNGasO2yHCOWfLBYLR48ezbDvxIkTpKSkOCiiQmblSnNw1PLl4OkJH34IS5cqQRURwEFJ6tKlS6latSpubm7ceeed7N27F4DIyEiaNm1KQEAAY8eOxTA0hY4UD/FXU9h85AIA99Yth4sl78uburpY6Fi3HABzNx4nLlGJk729+uqr/N///R/dunVjxIgR9OjRg549e/Lyyy87OjTnlpJi9je99144fRrq1IEtW2DECLMvqogIDkhSDx8+zKBBg5g0aRLR0dFUrlyZwYMHY7Va6dKlC02aNCE8PJw9e/YwZ86cgg5PxCHW7o8hNd2gUqA3NcqWslu5tYN9Ke/rxWVrKnM3HbNbuWIaPHgwmzdvpkmTJqSlpdGgQQPWr1/P0KFDHR2a8zp8GO66C95+2+yL+sQTEB4ODRs6OjIRcTIF3id17969TJgwgYcffhiAJ598kk6dOrFixQri4uKYMmUK3t7eTJgwgeHDhzNo0KCCDlGkQF1KTGbL34Ob2tcph8WONUkuFgv31CrDoq1RzPrjKI/9K0wrUdlZo0aNaNSokaPDKBwWLIAnn4SEBPD3h5kzoUcPR0clIk6qwGtSH3jgAYYNG2bb3r9/P9WrVyciIoLmzZvj7e0NmFO87Nmz56blWK1W4uPjMzxECqO1B2JISzcICypJtTIl7V5+/RA/qgaV5FJiCgs2H7d7+cXZjz/+yMWLmj3hlhISoH9/6NvX/L5VK3O6KSWoIpIFhw6cSk5O5t133+Wpp54iPj4+w3rXFosFV1dXYmNjM7124sSJ+Pn52R6hoaEFFbaI3VyxprLtuPkeb1e7rF1rUa9xsVh48p5qAHy+/ghXU9Ls/hzF1YgRI9ixY4ejw3BuW7fC7bfD/Png4mLOe7pqFVSq5OjIRMTJOTRJHT9+PKVKleKJJ57Azc0NT8+Ma5N7eXmRmJiY6bXjxo0jLi7O9oiKiiqIkEXsauuxi6SmG1Tw9yIsyP61qNd0bxxCBT8vzl9OZvmu0/n2PMXNqFGjmDZtGmlpSvxvkJ4OkydDy5Zw6JCZlK5dC6+8Am7qciIit+awT4rffvuN6dOns3nzZtzd3QkMDCQyMjLDOQkJCXh4ZL5muaen5w1JrUhhkpqezqa/R/TfVS0oX2pRr3F3deHR5pWZ/Mt+5m0+zkNNKubbcxUnpUuX5vz589x+++0MGzaMkiX/949G//79HRiZg50+DQMGwG+/mds9e8KMGRCQ96nVRKT4cEiSeuTIER599FE+/fRT6tatC0DTpk354osvbOccO3YMq9VKYGCgI0IUyXe7Tlzk8tkTlEyOxTgaTeQpT/xKl6VcaFU8vErY/fkeaRrK1N8PEBF1iV0nL9EwF0uuSkZz5syx/cP8zTff2PZbLJbim6T+9BMMHGhO0l+ihLmS1OOPa2opEcmxAk9Sk5KSeOCBB+jevTvdunXj8uXLALRq1Yq4uDjmzZtH//79mTRpEu3bt8fV1bWgQxTJN4ZhcHzfLrat+Ym/tm3CK+Uq6cDyjf87x8XVjUo169OoVQfqNWuNq5t9VooKKuXJ/Q2CWbrzFPM3HWfy//nbpdzibPXq1Y4OwXlYrfD88zBtmrndqBEsXGjOgSoikgsWo4BnzP/+++958MEHb9h/9OhRdu7cSZ8+ffDx8SEtLY21a9dmWG4wK/Hx8fj5+REXF4evr6+9wxbJs927dzNp0iS2b99u22e4eVEhtAolfXxISbZy8ewpEmLP2477lS5Hm54Dqd+8TZ66A/S50xykEn7sIj2nb8LTzYU/X2yHv3fm3Wkkc9d/zsTFxbFnzx7b4M7AwEAaNGhAcHCwg6PMvVx/ju7bB717w86d5vaoUeY8qF5e+RKniBReOfmcKfCa1O7du990JakqVapw8OBBwsPDadmyJWXKlCng6ETsLzk5mWnTpjF79mwMw8DN3YNSNVtwJrAhjRs1pOcdGUc5Xzx3ishNq9m26kfiLpzl+8/eZtcfv9Nt8HOU8s9b95cmlQOoE+zL3tPxfBt+kiGtq+apvOKqU6dObN68GR8fH/z8/DAMg7i4OK5cucI999zDvHnzCAkJcXSY+c8wYNYsMylNTDSXM509Gx54wNGRiUgR4NDR/ZkJCQmhW7duSlClSDh37hz9+/dn1qxZGIZBly5dGPzWTM7X6ooRWJlmVW9cozywbAVad3uUEe/M5p4eA3B1c+dI5DZmvPIkJw/tzVM8FouFvs3NpPjr8CgtPZxLZcqU4fjx48TFxXHixAmioqKIj4/n0KFDlCpVigEDBjg6xIKRlASTJpkJart25tynSlBFxE6cLkkVKSqOHz9Or169iIiIwM/Pj48++oh33nmHQ5ddSUkzKOvjSWjAzQdIuXt60aprH5544xPKhoZxJf4S8995noM7/8xTXF0aVcDTzYVD5y6z62Rcnsoqrv7zn/9kOjdzWFgY77//Pn/88YcDonIAb2+z3+mkSfDrr1ChgqMjEpEiREmqSD44dOgQ/fr14/Tp01SpUoVvvvmGdu3aYRgG4X8vgdq0SmC2+pkGVajEoPFTqdbgDlKTrXz9wWtE/PFbrmPz9XKnY73yACzedjLX5RRnCxcuvOmxWbNmUaNGjQKMxsHuuMMcMOWiPyciYl+aUVnEzg4fPkz//v2JjY2lRo0azJo1i6Ags1l/18k4Tsddxc3FQuNK/tku08PTi0eefp0fZk1h98aV/PDFFDy9vKnd5K5cxdizSUWWRZxiWcQpxj9QB083zaKRE9OnT2fRokU0bNgQPz8/AGJjY4mIiCApKYlly5Y5OEIRkcJP//qK2NH58+cZOnQosbGx1KtXj7lz59oSVIBFW08AUK+CL94eOfsf0dXNjW6Dn6Px3fdhGOks+XQix/fvzlWcd1UPoryvF3FJKazcey5XZRRnERERvPHGG9SqVcs2T2q9evWYNGkShw8fpnnz5o4OUUSk0FNNqoidJCUlMXz4cKKjo6lUqRKff/45AdetsJOUnMYPEeaSpHdUyd0ofYuLC/f3H8mV+Esc2LGJr6e+ysDxUygbUiVH5bi6WHjw9hA+XXOY/247yf0NCu+0SY7g5+dH3759HR2GiEiRpppUETswDINx48axa9cu/Pz8mD59eoYEFeDXPWe4bE0lwNudsKCSNynp1lxcXenx5DhCa9bDmnSFbz94A2vSlRyX89Dt5tKoaw7EEJNgzXU8IiIi+UFJqogdfPnll/zyyy+4u7vz0UcfERYWdsM53+2IBuC20ABc8rhEpLuHJw+PfBXfwDJcPBvNsi/ey/F0UtXLlqJRqD9p6QY/7T6dp3hERETsTUmqSB5FRkYyefJkAMaOHcsdd9xxwznnEq6y7kAMQI4GTGXF28ePnsPH4+Lqxr5tf/DnL0tyXEbXRuaUQT9EnLJLTCIiIvaiJFUkDxISEhgzZgwpKSm0b9/+pv0Ul+08RbphJqhBpTzt9vwh1WrTofdQAH7/5gtOHzuYo+s7NwjGYoHw47GcupRkt7hERETySkmqSB5MnDiRqKgoQkJC+M9//nPTeU+XbDeb+nv83Q/Unu5o14U6d7TCSE/n+xnvkJqcnO1ry/t50bSyOYhLTf4iIuJMlKSK5NKaNWv47rvvsFgsTJ482TZf5j/tOxPPntPxuLtaeCAfRtFbLBbuHzCSkr4BnD91gtVL5uTo+i6NzJjU5C8iIs5ESapILsTHx/Pqq68CMHDgQBo3bnzTc7/fYSZ/bWqVJaCkR77E4+3jxwODRgOw+ZclnMjB/Kmd6gfjYoGIk3GcuJCYL/GJiIjklJJUkVx4++23OXfuHFWqVGHUqFE3Pc8wDFsNZffGIfkaU83GzWnUqgMYBj/MnkpqSvaa/cv4eNKymrngwI+7VZsqIiLOQUmqSA79+eefLFmyBIvFwltvvYWXl9dNz90RdYnoS0mU9HClTa2y+R5bh97DKOUXyMUzJ/lj+TfZvu6Bhtea/NUvVUREnIOSVJEcSE5O5o033gCgV69e3H777Vmef60W9d665Sjh4Zrv8Xl5l6RDH3O0/x8/LuLCmZPZuq5T/fK4uVjYezqewzGX8zNEERGRbFGSKpIDc+fO5ciRI5QuXZrRo0dneW5ausHyXWbNZJe/5yMtCHWb3U21+k1IS03hp7kfZmuSf39vD1rV+LvJX7WpIiLiBJSkimRTdHQ0n3zyCQD//ve/8fX1zfL8LUcvci7Biq+XG61qlCmIEAFztP99/Ufi5u7Bsb072bNlbbaue6Dh3xP77zqV49WrRERE7E1Jqkg2vfPOO1y9epWmTZvSpUuXW57/wy6zqf+++sF4uBXsr1pA2WBadn4EgJXfzOLq1au3vObeeuXwcHPh0LnLHDirJn8REXEsJaki2RAeHs6vv/6Ki4sL48ePv+mk/dekpKWzYnfBN/Vfr+V9PfENDCLuwlnmzp17y/N9vdxp/XeN74pINfmLiIhjKUkVuYX09HTefvttAP7v//6PmjVr3vKaPw6dJzYxhaBSHjSvGpjfIWbK3dOLtj0fB2DGjBmcO3fultfc36A8ACt2n8nX2ERERG5FSarILfz4449ERkZSsmRJRowYka1rrk3ldH+DYNxcHfdrVr9FG0Kq1SExMZFp06bd8vx2dcrh7mph/9kEDp1Tk7+IiDiOklSRLCQlJfH+++8D8MQTTxAUFHTLa66mpPHrX2ZNpKOa+q+xWCx06G1OSfXdd9/x119/ZXm+Xwl3/lXdfI0/q8lfREQcSEmqSBbmzJnDmTNnCA4Opn///tm6Zu2BGBKsqQT7edGkUkA+R3hrFavXoUuXLhiGwcSJE285cv++BubE/j+pyV9ERBxISarITcTExPDFF18A8Oyzz2a5stT1rk3g/0DDYFxcsh5gVVCeeeYZvLy82LZtG2vWrMny3A51y+HmYmHP6XiOnb9SMAEWY5GRkTRt2pSAgADGjh2brem/1q5dS506dQgKCmLKlCkZjs2YMYPg4GDc3d3p0KEDp0+rRlxECiclqSI38cknn5CYmEjDhg25//77s3VNYnIqK/eaA5Qc3dR/vetrgqdMmUJaWtpNz/X39qBFtdIArIhUbWp+slqtdOnShSZNmhAeHs6ePXuYM2dOltfExMTQtWtXevfuzaZNm1iwYAGrV68GYMOGDbz88svMnz+fo0ePcvXqVZ577rkCeCUiIvanJFUkEydPnmTx4sWAWYt6qymnrvl97zmSUtKoXNqbBiF++Rlijj3++OP4+flx6NAhfvjhhyzPva++2eSvqajy14oVK4iLi2PKlClUq1aNCRMmMHPmzCyvWbBgAcHBwbz88svUqFGDV155xXbN/v37+fTTT2nfvj0VK1Zk0KBBhIeHF8RLERGxOyWpIpn45JNPSE1NpWXLljRr1izb111r6u/SsEK2E9uC4uvry+DBgwH48MMPSU5Ovum5HeqVw8UCu07GEXUxsaBCLHYiIiJo3rw53t7eADRs2JA9e/bc8pq2bdva3l/NmjVj+/btgPmPSI8ePWzn7t+/n+rVq9+0LKvVSnx8fIaHiIizUJIq8g9Hjx5l6dKlADz99NPZvi4uKYW1+2MA52rqv96jjz5K2bJlOXXqFF9//fVNzwsq5cmdYWaT/89q8s838fHxhIWF2bYtFguurq7ExsZm+xpfX1+io6NvOO/ChQt89tlnPPXUUzcta+LEifj5+dkeoaGhuXwlIiL2pyRV5B8++ugj0tPTadu2LQ0bNsz2db/+dYbktHRqlitFrfI++Rhh7pUoUYLhw4cDMH36dK5cufnAqGsT+/+kJv984+bmhqenZ4Z9Xl5eJCbevPb6n9fc7PynnnqKli1b0rlz55uWNW7cOOLi4myPqKioXLwKEZH8oSRV5Dr79+/np59+AmDkyJE5uvaHXX8vg9rQOWtRr3nwwQepXLkyFy9ezHKQTsd65bFYYMeJS5y6lFRwARYjgYGBxMTEZNiXkJCAh4dHtq/J7PxZs2axbt06Zs2aleXze3p64uvrm+EhIuIslKSKXOfDDz8EoFOnTtSuXTvb1124bOWPQ+cBeMBJm/qvcXd3t3VjmDVr1k2blsv6etG0srmkq5r880fTpk3ZvHmzbfvYsWNYrVYCA2++lO4/r9m5cychISG27S1btjB69GgWLVpEuXLl8idwEZECoCRV5G+7d+9m5cqVuLi45LgWdUXkGdLSDRqE+BEWVDKfIrSfjh07UqeOuVzqtblgM3Pf303+GuWfP1q3bk1cXBzz5s0DYNKkSbRv3x5XV1fi4+NJSUm54ZquXbuyYcMGVq9eTWpqKu+++y4dO3YE4OzZs3Tp0oXnn3+eJk2acPnyZS5f1vK2IlI4KUkV+dsHH3wAmElA1apVc3Ttsr9H9Xd18lrUa1xcXGy1qQsWLODcuXOZntepvpmkhh+P5Wz81QKLr7hwc3NjxowZDBs2jHLlyrF48WImTZoEmCP9ly9ffsM1QUFBvPfee3Ts2JHg4GAiIyMZP348AAsXLuTcuXOMHz8eHx8f20NEpDBSkioChIeHs2HDBtzc3LIcDZ2Z03FJbD12EYDODYPzI7x80bp1axo3bozVauXTTz/N9JxgvxLcXskfw4Bf/lKTf37o3r07Bw8eZMaMGezdu5d69eoBZtN/9+7dM73mqaeeYs+ePcyaNYtdu3bZmvVHjx6NYRg3PERECiM3RwcgzumrP0/k+to+d1ayYyT5zzAMpk6dCsBDDz2U7Wl4rt2jDQdjMAyoXNqbNftjbnGV87BYLIwePZoBAwawePFiHn/8cSpWrHjDefc3CGb7iUv8tPs0/VtUKfhAi4GQkJAM/Uqzo3r16lnOgSoiUtipJlWKvY0bN7Jt2zY8PDwYNmxYjq/fFR0HQKOK/naOLP81a9aMFi1akJqayscff5zpOdea/LccvUhMgrUgwxMRkWJMSaoUa4Zh2Pqi9urVi/Lly+fo+guXrZyMTcLFAvWdbBnU7Bo9ejQAy5Yt4/DhwzccrxjgTaOKfqQb8OseNfmLiEjBUJIqxdqaNWvYtWsXJUqUYMiQITm+/lotarUypSjlWTh7zzRs2JB27dqRnp7ORx99lOk59zUw+9qu2K0kVURECoaSVCm20tPTmTZtGgB9+/YlKCgox2XsOnkJgIYVC2ct6jWjRo3CYrHw888/Z7p2/H1/N/lvOnKBi1eSCzo8EREphpSkSrH1yy+/sH//fkqVKsVjjz2W4+vPxF/lbLwVVxcLdYMLd5Jas2ZN7r//fuB/U3Fdr3LpktSr4EtausFvavIXEZECoCRViqXU1FTb6lIDBw7E398/x2Vcq0WtWc6HEh6udozOMUaMGIGrqytr165lx44dNxy//+8m/5/U5C8iIgVASaoUSz/++CNHjx7Fz8+PAQMG5Ph6wzDYddLsj1rYm/qvqVKlim1ezmvdIK53rcn/j0PniUu8cSUkERERe1KSKsVOcnKybbqlwYMHU6pUqRyXsetkHBevJOPuaqFOeV97h+gwTz31FO7u7vz5559s2rQpw7GqZUpRu7wPqekGv+0966AIRUSkuFCSKsXOd999x8mTJwkKCqJPnz65KuOHv5dBrRPsi4db0fk1qlChAo888ggAU6dOvWG1omtzpq7YfbrAYxMRkeKl6Px1FcmG65cAHTp0KN7e3jkuIz3d4MddZpJWGCfwv5UnnngCLy8vdu3axZo1azIcu9Yvdf3B88RfVZO/iIjkHyWpUqx8/fXXnD17luDgYB5++OFclbHpyAXOxF/Fy92FGmVz3lXA2ZUpU4a+ffsCZt/U9PR027EaZUtRrUxJktPSWbX3nKNCFBGRYsBhSeqFCxcICwvj2LFjtn2RkZE0bdqUgIAAxo4de0NTo0heJCYmMmPGDACGDRuGh4dHrsr57/aTADSs6I+ba9H8P+/xxx+nVKlS7N+/nxUrVtj2WyyW60b5q8lfRETyj0P+wp4/f54HHnggQ4JqtVrp0qULTZo0ITw8nD179jBnzhxHhCdF1Pz587lw4QKVKlXiwQcfzFUZV6yp/BxpTsF0e6i/HaNzLv7+/gwaNAiAjz76iNTUVNux++qbSeqaAzFctqZmer2IiEheOSRJ7dWrF7169cqwb8WKFcTFxTFlyhSqVavGhAkTmDlzpiPCkyIoLi7O9n4aOXIk7u7uuSrn58gzJCanERZUktDAnPdnLUwGDBhAQEAAx44dY+nSpbb9dYJ9qFLam+TUdFbvU5O/iIjkD4ckqTNmzODpp5/OsC8iIoLmzZvbBrI0bNgw0+UZr7FarcTHx2d4iNzMrFmzSEhIyLCyUm5ca+rv0TgEi8Vir/CcUsmSJRkyZAgAH3/8McnJ5nKoFouF+/5u8l8RqSZ/ERHJHw5JUqtWrXrDvvj4eMLCwmzbFosFV1dXYmNjMy1j4sSJ+Pn52R6hoaH5Fq8UbjExMcyfPx+Ap59+GheX3L3toy8lsenIBQC6Nw6xW3zOrHfv3pQtW5bTp0/z7bff2vbf/3eT/+p9MSQmq8lfRETsz2lGfbi5ueHp6Zlhn5eXF4mJiZmeP27cOOLi4myPqKiogghTCqEZM2aQlJREo0aNaNOmTa7L+X5HNIYBzasGFvmm/mu8vLwYNmwYANOnTycpKQmA+iG+VAwoQVJKGmv3xzgyRBERKaKcJkkNDAwkJibjH7uEhISbjsD29PTE19c3w0Pkn6Kjo/n6668BeOaZZ3LdRG8Yxv+a+m+vaLf4CoOHHnqIkJAQzp8/z4IFC4CMo/x/1Ch/ERHJB06TpDZt2pTNmzfbto8dO4bVaiUwMNCBUUlh9/HHH5OSkkKLFi248847c13OzqhLHIm5gpe7iy05Ky48PDwYMWIEAF988QUJCQkAPNDQvA+/7zlLgib2FxERO3OaJLV169bExcUxb948ACZNmkT79u1xdXV1cGRSWB0+fNg2Kn306NF5KmvJ9mgAOtUrTylPt7yGVuh06dKFqlWrEhcXx9y5cwFoEOJH1TIlsaam88tfZx0coYiIFDVOk6S6ubkxY8YMhg0bRrly5Vi8eDGTJk1ydFhSiH344Yekp6fTrl07GjZsmOtyrqaksSziFFD8mvqvcXV1ZeTIkQDMmTOH2NhYLBYLD95mDiD7fke0I8MTEZEiyKFJqmEYVKlSxbbdvXt3Dh48yIwZM9i7dy/16tVzXHBSqEVGRvLLL79gsVgYNWpUnspaEXmauKQUQvxLcFf1IDtFWPh06NCBOnXqcOXKFb744gsAuv2dpP5x+Dxn4686MjwRESlinKYm9ZqQkBC6detGmTJlHB2KFFKGYfDOO+8A0LVrV2rWrJmn8hb+ac4c0atpKK4uRXtu1Ky4uLjY5jdesGAB586do1Jpb+6oHIBhwLKdpxwcoYiIFCVOl6SK5NXq1avZunUrnp6eNywakVOHzl1my7GLuLpY+L87NBdv69atady4MVarlenTpwP/mzP2OzX5i4iIHSlJlSIlJSWFd999F4CBAwcSHJy3kfiLtpwAoG3tspT388pzfIWdxWKxDUJbvHgxJ0+epHODYNxdLew5Hc++M1r5TURE7ENJqhQp3377LUePHiUwMJDBgwfnqayrKWm2uVH7NKtkj/CKhGbNmtGiRQtSUlKYMmUKASU9aFOrLACLw086ODoRESkqlKRKkZGQkMBHH30EwIgRIyhVqlSeyvvlrzPEJqZQwc+L1jXVR/p6Y8eOxWKxsGLFCrZv384jTc2uEEt2RJOcmu7g6EREpChQkipFxhdffEFsbCxVq1alZ8+eeS5vzsZjAPRqVqlYD5jKTJ06dWz3eMKECbSqXpqyPp5cvJLMyr2aM1VERPJOSaoUCSdPnrRNMv/cc8/h7u6ep/J2Rl1ix4lLeLi60FtN/Zl6+umnKVmyJH/99RfLf/yBh5qYc8h+Ex7l4MhERKQoUJIqRcKkSZOwWq00b96ce+65J8/lzfnjKAAPNAqmjI9nnssrikqXLs2TTz4JwJQpU3igrrmE8doDMZyJ05ypIiKSN0pSpdBbv349K1euxM3NjZdeegmLJW9N8+fir7J892kABrUMs0eIRVa/fv2oVKkS58+f57clX9EsLJB0A9uAMxERkdxSkiqFWnJyMm+99RYAffv2pXr16nku88s/T5CSZnBH5QAaVPTLc3lFmYeHB//+978BmD17Nu0ruQGwaOsJ0tINR4YmIiKFnJJUKdTmzp3L8ePHCQoKYvjw4Xku72pKGl/9eRyAgXdVyXN5xUHbtm1p0aIFycnJ/PnfGfh6uRF1MYm1B845OjQRESnElKRKoXXmzBk+/fRTwJwSKa9TTgEs3naS85eTqeDnRcd65fNcXnFgsVh4+eWXcXd3Z+MfG2jubi6AMG/TcQdHJiIihZmSVCm03n77bZKSkmjSpAldunTJc3mpael8tu4wAE+0roq7q349sissLIwnnngCgMgVc7GkJrFmfwzHzl9xcGQiIlJY6a+wFEqrV6/m559/xsXFhfHjx+d5sBTAj7tOE3UxidIlPXikqaadyqkhQ4ZQuXJlYi9coPKpNQB8uVm1qSIikjtKUqXQSUhI4PXXXwdg0KBB1K5dO89lpqcbfLLmEACP/SuMEh6ueS6zuPH09OS1114D4NzOVVhiT/BNeBSJyamODUxERAolJalS6EyePJmzZ89SuXJlRowYYZcyV+47x4Gzlynl6Ubf5pXtUmZx1Lx5c7p06YJhGJSKXEJ8opUl26MdHZaIiBRCSlKlUNm8eTPffvstAG+++SZeXl55LjM93WDq7wcA6Nu8Mn4l8rZaVXH3/PPP4+fnR+rFk7gdWsMX649oOioREckxJalSaCQmJvLKK68A0KtXL5o2bWqXcpfvPs1fp+Ip5enGE62r2qXM4qx06dKMGzcOAPf9v3L86GF+jjzj4KhERKSwUZIqhca0adOIiooiODiYZ5991i5lpqSl896v+wFzRH9gSQ+7lFvcde3alXbt2kF6Gh7bF/Hpqv0YhmpTRUQk+5SkSqGwceNG5s2bB8Brr71mlzlRAb4Jj+LYhURKl/TgsX9pCVR7sVgsvPrqq/j6+eESF82+NUvYePiCo8MSEZFCREmqOL3Y2Fhb8/EjjzxC69at7VJuUnIaH6w8CMCIttUp5elml3LFVKZMGV55+WUA3Pb/zuQFKxwckYiIFCZKUsWpGYbBK6+8wrlz5wgLC+P555+3W9mfrDnE2XgrIf4l6HOn5kXND507d6Z9p85YMNi79BPWRR5zdEgiIlJIKEkVp/b111/z+++/4+7uzuTJkylRooRdyj16/gqfrT0CwMsP1MHTTfOi5pdJ/3mdkqXL45J0iX+/+DLp6emODklERAoBJanitHbv3s2ECRMAGD16NPXq1bNLuYZh8MrSSJLT0rm7Zhk61itvl3IlcyVLluSddyZjWFyIOxjOm9NmODokEREpBJSkilOKjY3l6aefJiUlhXbt2jFo0CC7lf3LX2dYf/A8Hq4uvNa1nl2WVJWstW15B7d37g/Aoi8+Ytu2bQ6OSEREnJ2SVHE6aWlpPP/885w+fZpKlSoxYcIEuyWSsVeSeWXpXwAMu7sqYUEl7VKu3Nq0V56G0MaQnsZTI58mJibG0SGJiIgTU5IqTmfy5MmsX78eT09Ppk2bhq+vr13KNQyDF7/bzbkEK9XKlOSpNtXtUq5kTxkfL/qP+DfpPuWIj73AyFGjsFqtjg5LRESclJJUcSrffPMNc+fOBWDixInUrl3bbmUv2R7NisgzuLlYmNarMV7uGixV0EZ1rI9P2ycw3LyI2LmTl156SZP8i4hIppSkitPYtGkTb775JgAjR47kvvvus1vZJy4k8uoys5n/mXtrUj/Ez25lS/aV9HTjld73kNxsAIbFheXLl/PBBx84OiwREXFCSlLFKezevZsRI0aQmppK586defLJJ+1WdsLVFAbP28playp3VA5g2N3V7Fa25FyXhsE0bXYnKY16AjB9+nS++eYbB0clIiLORkmqONzhw4d54oknSExMpHnz5nYdKJWWbjB60U4OnL1MWR9PPupzO64uGs3vSBaLhde71YOwO0mp2Q4wl7pdvny5gyMTERFnonUgheTUdE5cvMKZOCuxicnEJaWw5ehFXCwW3FwtlPJ0o5SnG4ElPezej/P48eM8/vjjXLp0iQYNGvDRRx/h4eFhl7INw+Dtn/exct85PNxcmNH/Dsr7edmlbMmb2uV9eaJ1VT5N60QJw0rqwQ08//zzlChRgrZt2zo6PBERcQJKUosZwzDYfzaBrUcvEn48ll0n4zhxMZG09OwNXvH1cqOcrxehgd5ULu1NpUDvXK/WdPToUQYOHMi5c+eoVq0an332GSVL2mdKKMMweP+3A8xYZ64qNblnQ24L9bdL2WIfo9vXYPW+c+yjK5U8DWIi/2D06NG899573HvvvY4OT7IhLQ3Wr4fTpyE4GFq1AleNRxQRO1FzfzGQnm6w8dB5xn+/m7smraLT1PW8vPQvlu48xdHzV0hLNyjp4UrNcqVoFhbIvXXLUTfYlzrBvlQvU4pgPy9Kepr/z8RfTeXgucus2neO2X8c4z/L9zL7j6NsOnye2CvJ2Y7p4MGD9O/fn3PnzlG9enXmzJlDQECAXV6vYRhM+e0AH6w6BMD4znXodluIXcoW+/F0c+X9R27D3c2VE2FdqdesNSkpKTzzzDMsW7bM0eEVmMjISJo2bUpAQABjx47N1mwHa9eupU6dOgQFBTFlypRsH7OnJUugShVo0wb69DG/Vqli7hcRsQfVpBZhh84lsGR7NN/viOZU3FXbfi93F5pWCaRJ5QBurxRAzXI+lPP1zNAP9Ks/T9xQXlJyGjEJVzkVd5XjF65w/EIil5JSOHjuMgfPXeaHXaep4OfFFWsqXRpVuGnT+tatWxkxYgTx8fHUrl2bmTNnEhgYaJfXnJyazus//MWCv+Mf37kOg1tVtUvZYn91gn155t6avPPzfnaHdKFtRx/W/LKcF154gfj4ePr27evoEPOV1WqlS5cudOzYkUWLFjFq1CjmzJmT5QprMTExdO3alWeffZbevXvTq1cvGjduTJs2bbI8Zk9LlkDPnvDPfDo62ty/eDH06GHXpxSRYshiFJFJCuPj4/Hz8yMuLs5uk78XRunpBmsOnGPGuiNsPnLRtt/Xy43ODYPpULc8LaqVvmXf0syS1H8yDIOYBCv7ziSw70wCJy5e4VqvAYsFmoeVptttFbivfjB+3u4ALF++nHHjxpGSksJtt93GJ598Yrca1LPxV3nyy21sP3EJiwVeeaAug+4Ks0vZmcnOPXIWfe6s5OgQbiot3WDw3K2s3h9DBT9P2l79g/9+swiAvn378vzzz+Pm5hz/T9v7c+b777/nscce4+TJk3h7exMREcHw4cPZsGHDTa+ZOnUq06dPZ+/evVgsFpYuXcq3337Ll19+meUxe72+tDSzxvTkyczLsFigYkU4elRN/yJyo5x8jjrHJ7/k2dWUNL7fEc3n649wOOYKAK4uFtrUKkOP2yvStnZZuw96slgslPX1oqyvF61rliHRmsruU3GcupTE1mOxbDpygU1HLvDK0r+4u0Zp3Pf/wurvvwKgQ4cOvP3223h55X0gU3q6weJtJ5n08z4uXknG18uNab0a06Z22TyXLfnP1cXC1F6N6f7xHxw9f4UDVe5l9JgKTJ0yhS+//JLjx48zefJk/PyK3ty2ERERNG/eHG9vbwAaNmzInj17bnlN27ZtbS0fzZo1Y9y4cbc8lhmr1Zph1a/4+Phbxrx+/c0TVDBrV6OizPPuueeWxYmI3JSS1ELu4pVkvtx8nHmbjnH+stkn1MfTjT53VmLgXVUI9itRYLF4e7pxZ1hp+txZiZOxiSyLOMWynafYd/wMa2d9imvMQQAq3nk/7QY9Q7pL3t5+hmGw6fAFJv+6nx0nLgFm8/H0vrdTubR9BmBJwfAr4c6Mfk3o/vEfbDkWS5mGTXj//amMG/cC69ev56GHHuL999+nQYMGjg7VruLj4wkL+19tv8ViwdXVldjY2Ju2MMTHx1O3bl3btq+vL9HR0bc8lpmJEyfy+uuv5yjm06fte56IyM0oSS2kjp6/wswNR1i87SRXU9IBCPEvwaC7qvBI01B8vNwdGl/FAG+euqc6DVxP8/ziD4m9cB7cPEhu9H8cLN+YUYsi8HTbTbOwQFrXKMOdVQOpXd4XD7esx/IZhsHxC4ms2X+Or7ac4MDZywCU9HBldPuaDLyrCu6uGg9YGNUo58PHj97OkHnhLN91mhJNKjJ//peMGfMMUVFRPProo4wZM4Z+/frhWkTakd3c3PD09Mywz8vLi8TExJsmqf+85tr5tzqWmXHjxjFmzBjbdnx8PKGhoVnGHByc5eEcnycicjNKUgsRwzDYdjyWGeuO8Nves7ZBC/VDfBnSqir3Nwh2mgTtypUrTJ48ma+//hqAqlWr8v7775NSqhzLd51m+e7THL+QyPqD51l/8DwAHq4u1CxfikqB3gT7lSCwpAcWi9l8eC7+KtGXrrL3dDzRl5Jsz+Pt4UqP20MY2bYG5Xw1B2phd0+tsnzYuzHDv9rB4m0ncXetxNfffMsrL4/n999/5+233+b333/nrbfeonLlyo4ON88CAwOJjIzMsC8hISHLuYIDAwOJiYnJ9PysjmXG09PzhiT5Vlq1MvucRkffOHAK/tcntVWrHBUrInIDJamFQGpaOr/uOcuMdUfYGXXJtr9d7bIMblWV5lUD7bZCU14ZhsHy5ct55513OHfuHAD9+vVjzJgxtv6n9Sr4MbZjLQ6eu8z6g+fZcDCGHVGXuJSYQmR0PJHRWfeLc3e10LhSAJ3qlafnHRXxdXCtsdhXp/rBvPt/aYz5JoKFW04Qk3CVae9O4cfvl/DOO++wbds2unfvzpAhQ3jsscfs0q/ZUZo2bcoXX3xh2z527BhWqzXL2S6aNm3KwoULbds7d+4kJCTklsfsxdUVpk0zR/Ff+yfymmsfQ1OnatCUiOSdRvc7sUuJySzaGsX8TcdttYcebi70aBzC4FZhVC/rk2/PnZuR69FH9rHym5kc37cLgEqVKvHaa6/RokWLW15rGAYnY5P461Q8py4lcTouifikVAwMDAOCfDyp4F+CKqW9aVI5AG8Px/9/pdH9+WvF7tOM/non1tR0GoT48Vm/JhhXLjJ+/Hg2b94MQEhICM899xwdOnTAxSX/WxHs/TmTmppKhQoVePfdd+nfvz/Dhg0jOjqaH374gfj4eEqUKIG7e8Z/ws6fP09oaCg//fQTrVq1onv37oSFhfHhhx9meczer2/JEnj66YyDqEJDzQRV00+JyM3k5HNGSaoT2ncmnrkbj/Hdjmhbf9MAb3f6Na9MvxZVKOOTs+a53MhJAnbm+GHWL1vAvm1/AODh4cETTzzB4MGDc9yUWJgoSc1/247HMmReOBevJONXwp2JPRpwX/3y/PTTT0yePJmzZ88CULt2bUaMGEGbNm3yNVnNj8+Z77//nj59+uDj40NaWhpr166lXr16VKlShalTp9K9e/cbrvnkk08YPXo0fn5+lCxZkj///JNy5crd8pi9X59WnBKRnFKSWgiT1ISrKSzfdZrF204SfjzWtr9OsC+D7qpC10YV7D6FVFZulYClp6dx9K8dbP75vxz5azsAFosLDe9qx3uvPW/3JkZnpCS1YBy/cIVRC3cQcTIOgB6NQxh3fx1KuqYxe/Zs5syZw+XL5gC6atWq0a9fP7p27UqJEvaf2SK/Pmeio6MJDw+nZcuWlClTJlvXHDp0iL1793L33XffEEtWx7JS2D9HRcT5KUktJB+u1tQ0/jh0nh8jTrMi8gxJKWkAuFigY73yDLorjKZVAhzS3/RmCdjFs9FEbPiNXX/8TvxFc4CGxcWFus1a06pLH8qEVC7UCVFOKEktOClp6Uz9/QCfrDmMYUApTzdGtK3OwJZVuHolgdmzZ/PVV1/ZklUfHx86duxIt27duP322+1Wu1oYP2dyoqi/PhFxPCWpTvzhGn81hY2HzvNz5BlW7j1HgjXVdqxqmZL8X5NQetwe4vCR6tcSMMMwOHfyGAcj/uTgzs2cPLTXdo5XyVI0bNmeOzs8iH+Z8rb9hT0hyi4lqQVv+4lYXl/2l61WNaiUJ4PuqkLfOyvjmm7lv//9L19++SUnr+soGRISQqdOnWjdujWNGze+oY9nThSWz5ncKuqvT0QcT0mqE324JiansutkHBsPX2DDwRgiTsaRlv6/W17Wx5OO9crTvXEIt1fyd/gofcMwOHHiBJ98+xtRB//iyF87iLtw9n8nWCxUq3c7jVp3pNZtLXDLZHqbopIQ3YqSVMdITzdYsiOaKb/u51TcVQC83F24t255ejQOoWW1QCJ2bGfp0qX88ssvXLlyxXZtqVKlaNGiBXfeeSdNmjShRo0aOZpz1Vk/Z+ylqL8+EXG8Qp2kRkZGMmjQIA4dOsTgwYN55513spW4OcOHa1xiCgfPJXDg7GV2R8exM+oSB84mZEhKAcKCStK2dlnuq1+e2ysF4OLimMT0ypUrREVFceDAAdtj7969nD9/PsN5bu4ehNW9jRqN7qRG4+b4BgRlWW5RSoiyoiTVsVLS0vlx1yk+W3uEfWcSbPtLebrRslppWtUIok6ZEpzet40N69eyYcMGLl68mKGMUqVK8corr9ClS5dsPaczfM7kp6L++kTE8XLyOeP4eXyuY7Va6dKlCx07dmTRokWMGjWKOXPmMGjQIEeHRlJyGrGJycQmJnMu3kr0pSRO2R5XOXrhCjEJ1kyvLefrSdMqgbSqEcRd1YOoGOCdb3EahkFycjJxcXHExsZy4cIFLl68yMWLFzl37hzR0dFER0dz8uRJYmNjMy3D3d2d8lVqUrFGXSrXbECVOo1w9yy8c1FK0eTu6sKDjSvS/bYQdp2M47sd0fy46xTnLyfz656z/LrHbAHwcHWhdmgX/jW6N15XTnHp6G7OHN7LoX2RXL58mbJlyzr4lYiISGacKkldsWIFcXFxTJkyBW9vbyZMmMDw4cPzLUkd/f58rlpTSE5Nw5qSTnJaOskpaSSnmdspaWkkJqdxxZpCSqoBXFcjaty47Qr4l3CjrK8Xwb6eVAwoQWhgib8nm7+AcfgA6w8ZXKu8vv6rYRikpaWRkpJCampqpl+v/z45OZnExESuXLnClStXbN8nJiaSmvq/fq634ufnR/Xq1alZsyY1a9akVq1a1K1bl//uPHvri0WcgMVioVGoP41C/XnlgbpEnopj7f4Y/jx6kd3RccQlpbDrZBy7/u7HCvWhYn0IeQi3hDNEWcpwp0NfgYiIZMapktSIiAiaN2+Ot7dZ09iwYUP27NmT6blWqxWr9X81l3Fx5h+g+PisVyu63vIZb2NJS87WudnttZbw9+NwtqPIHy4uLvj7+xMQEEBgYCD+/v6ULl2a4OBgKlSoQIUKFQgODs60qt1qtZJ4JSGTUrMnJz+Dwiwv96igFZefCUAVXxeqNC3HgKblbItEREbHcTjmCidjEzkZm8TJ2ETOX04m2dMfLxcj2/fn2nlO1kvKbq69ruL0fhGRgpWTz1GnSlLj4+MJCwuzbVssFlxdXYmNjSUgICDDuRMnTuT111+/oYzQ0NB8j1OyNsTRAcgN9DO5uS5Tc35NQkICfn5+do/F0RISzH+89DkqIvktO5+jTpWkurm53bBCkZeXF4mJiTckqePGjWPMmDG27fT0dI4fP85tt91GVFSUOv3nQXx8PKGhobqPdqB7aT/OcC8NwyAhIYEKFSo45PnzW4UKFYiKisLHx8fhM43YizO8b4oy3d/8V9TucU4+R50qSQ0MDCQyMjLDvoSEBDwymebI09PzhoT22oTdvr6+ReIH6Wi6j/aje2k/jr6XRbEG9RoXFxcqVqzo6DDyhaPfN0Wd7m/+K0r3OLufo/m3yHUuNG3alM2bN9u2jx07htVqJTAw0IFRiYiIiEhBc6oktXXr1sTFxTFv3jwAJk2aRPv27XM02baIiIiIFH5O1dzv5ubGjBkz6NOnD2PHjiUtLY21a9dm+3pPT09effXVG7oBSM7oPtqP7qX96F5Kbuh9k790f/Nfcb7HTrfiFEB0dDTh4eG0bNmSMmXKODocERERESlgTpmkioiIiEjx5lR9UkVEREREQEmqiIiIiDghJakiIlLkXbhwgbCwMI4dO+boUERypDi/dwtNkhoZGUnTpk0JCAhg7Nix2V47e+3atdSpU4egoCCmTJmS4ViXLl2wWCy2R/v27fMjdKeQm/uX1b3L6lhRZu/7WJzeg/+U29/pQ4cOZTp3cnF9T8qtnT9/ngceeKBY/pHPT7n9HZbsK+7v3UKRpFqtVrp06UKTJk0IDw9nz549zJkz55bXxcTE0LVrV3r37s2mTZtYsGABq1evth3ftm0bu3fvJjY2ltjYWJYuXZqPr8JxcnP/srp3t7qvRZW97yMUn/fgP+X2d/ro0aN07tyZ2NjYDPuL63tSsqdXr1706tXL0WEUKbn9HZacKfbvXaMQ+O6774yAgADjypUrhmEYxs6dO4277rrrlte9//77Rq1atYz09HTDMAzj+++/Nx599FHDMAwjKirKKF++fP4F7URyc/+yundZHSvK7H0fi9N78J9y+ztdp04d45133jH++dFVXN+Tkj2HDx82DMMwAOPo0aOODaaIyO3vsORMcX/vFoqa1IiICJo3b463tzcADRs2ZM+ePdm6rm3btlgsFgCaNWvG9u3bAdiyZQtpaWlUrFiRkiVL0qtXrxtqZ4qK3Ny/rO5dVseKMnvfx+L0Hvyn3P5O//jjj/zf//1fpuUVx/ek/E/37t3x9/e/4fHRRx9RtWpVR4dX5OT2d1hypri/d50qSb3Zh8wHH3xAWFiY7TyLxYKrq+st/6DHx8dnuM7X15fo6GgADhw4QJMmTfjll18IDw/n2LFjvPjii/nzwhzsn/chO/cvq3uX1bGizN73sTi9B/8pN/cSbv6BXVzfk/I/n332GTt37rzh0b9/f0eHViTl9ndYJCecalnUzz77jKSkpBv2T5s2zVZDco2XlxeJiYkEBATctDw3N7cMy4hduwbghRde4IUXXrAde/vtt+nZsyeffvppXl+G0/nnfYBb37+s7l1Wx4oye9/H4vQe/Kfc3MuclFdc3pPyP+XKlXN0CMWKvX+HRTLjVEnqzT5kypcvT2RkZIZ9CQkJeHh4ZFleYGAgMTEx2brG39+f8+fPY7Vai9z6uIGBgTm+f1ndu5zc16LE3vfxn4rye/CfcnMvb1VecXxPijiKvX+HRTLjVM39N9O0aVM2b95s2z527BhWqzXTaWiyum7nzp2EhIQA0LNnzwzHtm7dSvny5YtkcpCb+5fVvcvqWFFm7/tYnN6D/5Tb3+nslldc3pMijmLv32GRTDl65FZ2pKSkGGXKlDHmzp1rGIZhDB061HjggQdsx+Pi4ozk5OQbrouJiTG8vLyMVatWGSkpKUbnzp2NESNGGIZhGK+//rrRvHlzY/PmzcYPP/xglC9f3njjjTcK5gUVsKzuX27uXVbHijJ738fi9B78p9zcy2uOHj16w+j+4vqelJyhmI6Qzg+3+rss9lVc37uFIkk1DHO6ixIlShhly5Y1SpcubURGRtqOVa5c2fjuu+8yve7jjz823N3djaCgIKNy5crGmTNnDMMwjOTkZOOxxx4zfHx8jGrVqhmvv/66kZKSUhAvxSFudv9yc+9udawos+d9LG7vwX/Kzb00jMyTVMMovu9JEUfJ6u+yiD1YDKPwLBERHR1NeHg4LVu2pEyZMtm+7tChQ+zdu5e7774bX1/ffIzQueXm/mV174rrfbX3fSzOcvs7fTO6zyIFy96/wyLXK1RJqoiIiIgUD4Vi4JSIiIiIFC9KUkVERETE6ShJFRERERGnoyRVRERERJyOklQRERERcTpKUkVERG5izpw5WCyWGx5r1qxhzZo1VKlSxdEhZtucOXO45557nL7MgpbVaygKr68wU5IqIiJyE3369CE2NpZ169YBEBsbS2xsLP/6178cHFnmLBYLx44dy/RYnz59+PHHHws2ICeh+1I4uTk6ABEREWfl4eGBh4cHPj4+APj7+zs2oDy49lokI90X56WaVBERkTxYtmwZlStXJiAggA8++MC2f+vWrdx55534+fnRo0cP4uLibMfWrVvHbbfdRkBAAH369OHSpUu2Y/fccw9z5sxhypQpVK5cmWXLltmO/fzzzzRo0AB/f38GDx6M1WoFoHbt2lgsFgDCwsKwWCwsWrQoQ5w3a7peuHAhNWrUwM/Pj169emWIc/r06YSGhuLj40P37t1JSEjI070yDINXX32VMmXKUKdOHYYMGWKL6bXXXmPgwIG2c//ZneJmsVy77o033sDf35/KlSuzfv36PN+XrMybN48aNWoQFBTEiy++yLV1kQzD4NlnnyUoKIiAgACefvpptGZS7ilJFRERyaULFy4wadIkli9fzuuvv87YsWNJSkri0qVL3HfffXTu3Jndu3eTmJjIs88+C0BUVBT3338/w4cPZ9u2bVy+fDlDcgbw2WefsWrVKj7//HNatmwJwOHDh+nWrRvPPPMM27ZtY9u2bUyePBkwE+LY2FgAIiIiiI2N5aGHHrpl/Js2bWLIkCFMmTKFiIgIzp49y6uvvgrA7t27GTFiBLNnz2bv3r2cO3eOTz75JE/3a8mSJXz88cd89913zJo1i++//z5b190qlp9++olDhw6xfft27rrrLl566SUg9/clK+vXr2fIkCFMnTqVNWvWMH/+fBYsWADAL7/8wuzZs1m5ciUbNmzgu+++49dff83T8xVnau4XERHJpcuXL/Ppp59Sv359atasydNPP825c+dYv3497u7uvPzyy1gsFp555hn69esHwJdffknLli0ZMmQIYNYQhoSEcObMGcqXL28rd926dbi7u9uea+HChTRu3JjHHnsMgGHDhjFz5kzGjx9v644A4Ovrm+1uCbNnz6Zfv3506dLFFsupU6cAqFGjBmfOnMHd3Z0tW7ZgGAYHDhzI0/1aunQpffv2tfXpHTJkCBs3brzldbeKxdXVlRkzZuDl5cXAgQMZOnQoQK7vS1bmzp3Lgw8+SOfOnQHo27cvy5Yto2/fvpQoUYL09HSsViv169fn2LFjtppcyTklqSIiIrkUEBBAo0aNAGz9Gg3DIDo6mpiYGAICAgBIT08nISGBq1evEhUVRdWqVW1lVKhQAU9PT6KiomxJ6rBhwzIkqADR0dFs377dlmilpqZSqlSpPMV/8uRJ7r77btt2rVq1qFWrFgBJSUkMHjyYtWvX0rhxY9zc3EhLS8vT8507d4769evbtitXrnzTJDUxMdH2/a1iadGiBV5eXoD5c8jPJvbo6GhWr15t+zkkJyfTsGFDAO6++25efPFFBg0axKlTp+jZsydTp06lZMmS+RZPUabmfhERkVzy9fXNdH/FihW544472LlzJzt37iQiIoIdO3bg7u5OpUqVOHLkiO3c6OhorFYrlSpVsu3LLKmpWLEiXbt2zVDmb7/9luEci8WSowQtNDSUo0eP2rZXr17NfffdB8C0adOIiYnh7NmzrFq1ihYtWmS73JsJDg4mKirKtn39fbBYLBkSz/DwcNv3t4rlZj+H68u2V+JasWJFhg0bluHnMHfuXAAOHTpEly5d+Ouvv4iMjGTz5s1Mnz7dLs9bHClJFRERsbPOnTtz/PhxtmzZgqurK4sWLaJTp04YhkHfvn3ZuHEjn3/+OUePHuXJJ5+ke/fulCtXLssye/fuzfr16zl48CBgJm6DBg3KcE716tVZvnw50dHRtmmzsjJo0CC+/PJLfvzxR44ePcrEiRNtyfLly5cxDIPz58/z1Vdf8emnn+Y50evevTtfffUVGzZsYNOmTcycOdN2rGLFimzduhWr1cqBAwf4/PPPbcfyGktO70tW+vfvz9KlSzlz5gypqam89NJLtj6wq1at4qGHHmLHjh0kJydjsVhIT0/P0/MVZ0pSRURE7Mzf359ly5bx3nvvUbt2bb777juWLVuGm5sbFStWZPny5Xz88cc0btyYkiVLMnv27FuWWbVqVebOncuYMWOoV68ekZGRLFy4MMM506dPZ+rUqVSvXp3PPvvslmU2b96czz//nGeeeYbbb7+d8uXL2wZjXRuZXrNmTWbPns3jjz/Ozp07c3U/runWrRujRo3iwQcfZPDgwfTq1ct2rFevXlSqVImaNWsyZMgQJk6caDuW11hyel+y0qpVK1577TX69etH48aNSU5Otg3iGjRoEK1bt6Zjx440bNiQGjVq8OSTT+bp+Yozi6G5EURERMQB5syZw5w5c1izZo2jQxEnpJpUEREREXE6qkkVEREREaejmlQRERERcTpKUkVERETE6ShJFRERERGnoyRVRERERJyOklQRERERcTpKUkVERETE6ShJFRERERGnoyRVRERERJzO/wNzOFDlEVUGRQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['age_range'])['label'].mean()]\n",
    "plt.figure(figsize=(8,4))\n",
    "ax=plt.subplot(1,2,1)\n",
    "sns.distplot(repeat_buy,fit=stats.norm)\n",
    "ax=plt.subplot(1,2,2)\n",
    "res = stats.probplot(repeat_buy,plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "56d9864a-1c4a-488b-a19e-18bfd42b3854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>action_type</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>234512</td>\n",
       "      <td>146770</td>\n",
       "      <td>1173</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>234512</td>\n",
       "      <td>146770</td>\n",
       "      <td>1173</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3271.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>234512</td>\n",
       "      <td>1106076</td>\n",
       "      <td>992</td>\n",
       "      <td>3783</td>\n",
       "      <td>8165.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>234512</td>\n",
       "      <td>1106076</td>\n",
       "      <td>992</td>\n",
       "      <td>3783</td>\n",
       "      <td>8165.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>3271.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>234512</td>\n",
       "      <td>866567</td>\n",
       "      <td>1198</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type  \\\n",
       "419   234512   146770    1173        693    3186.0         625            0   \n",
       "420   234512   146770    1173        693    3186.0         625            0   \n",
       "421   234512  1106076     992       3783    8165.0        1016            0   \n",
       "422   234512  1106076     992       3783    8165.0        1016            0   \n",
       "423   234512   866567    1198        693    3186.0         625            0   \n",
       "\n",
       "     merchant_id  label  \n",
       "419       3018.0    0.0  \n",
       "420       3271.0    0.0  \n",
       "421       3018.0    0.0  \n",
       "422       3271.0    0.0  \n",
       "423       3018.0    0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_1=user_log.merge(train_data,on=['user_id'],how='left')\n",
    "all_data_1[all_data_1['label'].notna()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f5c52ee-42a5-49c4-b49f-6f57be484255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>511</td>\n",
       "      <td>943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>512</td>\n",
       "      <td>975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>513</td>\n",
       "      <td>1221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>514</td>\n",
       "      <td>1170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>515</td>\n",
       "      <td>1260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_stamp   label\n",
       "0         511   943.0\n",
       "1         512   975.0\n",
       "2         513  1221.0\n",
       "3         514  1170.0\n",
       "4         515  1260.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_2=all_data_1[all_data_1['label'].notnull()]\n",
    "all_data_2_sum=all_data_2.groupby(['time_stamp'])['label'].sum().reset_index()\n",
    "all_data_2_sum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ebcd3d39-e0d6-423c-bb46-601221ece697",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_2_sum['time_stamp'] = all_data_2_sum['time_stamp'].astype(str)\n",
    "all_data_2_sum['label'] = all_data_2_sum['label'].astype(int)\n",
    "a = []  # 初始化一个空列表\n",
    "for i in range(len(all_data_2_sum)):\n",
    "    if len(all_data_2_sum['time_stamp'][i]) == 3:\n",
    "        a.append(all_data_2_sum['time_stamp'][i][0])\n",
    "    else:\n",
    "        a.append(all_data_2_sum['time_stamp'][i][0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "091cfe7a-a119-46f9-a1a4-5205d0f1fbf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_data_2_sum['month']=a\n",
    "all_data_2_sum=all_data_2_sum.astype(int)\n",
    "plt.figure(figsize=(20,8))\n",
    "c=5\n",
    "for i in range(1,8):\n",
    "    plt.subplot(3,3,i)\n",
    "    b=all_data_2_sum[all_data_2_sum[\"month\"]==c]\n",
    "    plt.plot(b['time_stamp'],b['label'],linewidth=1,color=\"orange\",marker=\"o\",label=\"Mean value\")\n",
    "    c+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39795fcf-88c1-4636-9bb6-d04de34e2e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23669"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del test_data['prob']\n",
    "train_data['target'] = 1\n",
    "test_data['target'] = -1\n",
    "# 使用 pd.concat() 来合并 train_data 和 test_data\n",
    "all_data = pd.concat([train_data, test_data])\n",
    "# 合并 user_info\n",
    "all_data = all_data.merge(user_info, on='user_id', how='left')\n",
    "# 删除不再需要的 DataFrame\n",
    "del train_data, test_data, user_info\n",
    "# 清理内存\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "007eba70-b733-48ee-9cb7-da3446d6a74f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender\n",
       "0    34176         3906    0.0       1        6.0     0.0\n",
       "1    34176          121    0.0       1        6.0     0.0\n",
       "2    34176         4356    1.0       1        6.0     0.0\n",
       "3    34176         2217    0.0       1        6.0     0.0\n",
       "4   230784         4818    0.0       1        0.0     0.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "581388dd-2fe4-4cbc-bb95-d46645350c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id             0\n",
       "merchant_id         0\n",
       "label          257626\n",
       "target              0\n",
       "age_range           0\n",
       "gender              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.dropna(subset=['age_range','gender'],inplace=True)\n",
    "all_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f0a4688-29e3-41c1-a2a3-d1ce1464b3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum\n",
       "0    34176         3906    0.0       1        6.0     0.0       451\n",
       "1    34176          121    0.0       1        6.0     0.0       451\n",
       "2    34176         4356    1.0       1        6.0     0.0       451\n",
       "3    34176         2217    0.0       1        6.0     0.0       451\n",
       "4   230784         4818    0.0       1        0.0     0.0        54"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户店铺数\n",
    "sell=user_log.groupby(['user_id'])['seller_id'].count().reset_index()\n",
    "all_data=all_data.merge(sell,on=['user_id'],how='inner')\n",
    "all_data.rename(columns={'seller_id':'sell_sum'},inplace=True)\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ff2fa1de-c0fc-4233-8613-11b3dfa7f762",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nunique_k(data, sigle_name, new_name_1):\n",
    "    data1 = user_log.groupby(['user_id'])[sigle_name].nunique().reset_index()\n",
    "    data_union = data.merge(data1, on=['user_id'], how='inner')\n",
    "    data_union.rename(columns={sigle_name: new_name_1}, inplace=True)\n",
    "    return data_union\n",
    "\n",
    "# 不同店铺个数\n",
    "all_data = nunique_k(all_data, 'seller_id', 'seller_id_unique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e76ce737-42ba-4a79-b95a-73e5d8c7abe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514762</th>\n",
       "      <td>228479</td>\n",
       "      <td>3111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514763</th>\n",
       "      <td>97919</td>\n",
       "      <td>2341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514764</th>\n",
       "      <td>97919</td>\n",
       "      <td>3971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514765</th>\n",
       "      <td>32639</td>\n",
       "      <td>3536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514766</th>\n",
       "      <td>32639</td>\n",
       "      <td>3319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514767 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0       1        6.0     0.0       451   \n",
       "1         34176          121    0.0       1        6.0     0.0       451   \n",
       "2         34176         4356    1.0       1        6.0     0.0       451   \n",
       "3         34176         2217    0.0       1        6.0     0.0       451   \n",
       "4        230784         4818    0.0       1        0.0     0.0        54   \n",
       "...         ...          ...    ...     ...        ...     ...       ...   \n",
       "514762   228479         3111    NaN      -1        6.0     0.0      2004   \n",
       "514763    97919         2341    NaN      -1        8.0     1.0        55   \n",
       "514764    97919         3971    NaN      -1        8.0     1.0        55   \n",
       "514765    32639         3536    NaN      -1        0.0     0.0        72   \n",
       "514766    32639         3319    NaN      -1        0.0     0.0        72   \n",
       "\n",
       "        seller_id_unique  \n",
       "0                    109  \n",
       "1                    109  \n",
       "2                    109  \n",
       "3                    109  \n",
       "4                     20  \n",
       "...                  ...  \n",
       "514762               278  \n",
       "514763                17  \n",
       "514764                17  \n",
       "514765                33  \n",
       "514766                33  \n",
       "\n",
       "[514767 rows x 8 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7031ba97-1407-43f9-ad98-69d9d583b636",
   "metadata": {},
   "outputs": [],
   "source": [
    "#不同品类个数\n",
    "all_data=nunique_k(all_data,'cat_id','cat_id_unique')\n",
    "#活跃天数\n",
    "all_data=nunique_k(all_data,'time_stamp','time_stamp_unique')\n",
    "#不用行为种数\n",
    "all_data=nunique_k(all_data,'action_type','action_type_unique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ea9932e7-4b1b-4fc7-a16b-937ba7adfa2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>action_type_unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514762</th>\n",
       "      <td>228479</td>\n",
       "      <td>3111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>278</td>\n",
       "      <td>71</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514763</th>\n",
       "      <td>97919</td>\n",
       "      <td>2341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514764</th>\n",
       "      <td>97919</td>\n",
       "      <td>3971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514765</th>\n",
       "      <td>32639</td>\n",
       "      <td>3536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514766</th>\n",
       "      <td>32639</td>\n",
       "      <td>3319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514767 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0       1        6.0     0.0       451   \n",
       "1         34176          121    0.0       1        6.0     0.0       451   \n",
       "2         34176         4356    1.0       1        6.0     0.0       451   \n",
       "3         34176         2217    0.0       1        6.0     0.0       451   \n",
       "4        230784         4818    0.0       1        0.0     0.0        54   \n",
       "...         ...          ...    ...     ...        ...     ...       ...   \n",
       "514762   228479         3111    NaN      -1        6.0     0.0      2004   \n",
       "514763    97919         2341    NaN      -1        8.0     1.0        55   \n",
       "514764    97919         3971    NaN      -1        8.0     1.0        55   \n",
       "514765    32639         3536    NaN      -1        0.0     0.0        72   \n",
       "514766    32639         3319    NaN      -1        0.0     0.0        72   \n",
       "\n",
       "        seller_id_unique  cat_id_unique  time_stamp_unique  action_type_unique  \n",
       "0                    109             45                 47                   3  \n",
       "1                    109             45                 47                   3  \n",
       "2                    109             45                 47                   3  \n",
       "3                    109             45                 47                   3  \n",
       "4                     20             17                 16                   2  \n",
       "...                  ...            ...                ...                 ...  \n",
       "514762               278             71                110                   3  \n",
       "514763                17             14                  8                   3  \n",
       "514764                17             14                  8                   3  \n",
       "514765                33             24                 15                   4  \n",
       "514766                33             24                 15                   4  \n",
       "\n",
       "[514767 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ceaf345b-725b-46f5-bfa4-55cff6988e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>action_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>328862</td>\n",
       "      <td>323294</td>\n",
       "      <td>833</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>328862</td>\n",
       "      <td>844400</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>328862</td>\n",
       "      <td>575153</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>328862</td>\n",
       "      <td>996875</td>\n",
       "      <td>1271</td>\n",
       "      <td>2882</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>328862</td>\n",
       "      <td>1086186</td>\n",
       "      <td>1271</td>\n",
       "      <td>1253</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type\n",
       "0   328862   323294     833       2882    2661.0         829            0\n",
       "1   328862   844400    1271       2882    2661.0         829            0\n",
       "2   328862   575153    1271       2882    2661.0         829            0\n",
       "3   328862   996875    1271       2882    2661.0         829            0\n",
       "4   328862  1086186    1271       1253    1049.0         829            0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e241188c-3829-487e-8cd9-457c1dc43ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>action_type_unique</th>\n",
       "      <th>time_stamp_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  time_stamp_unique  action_type_unique  \\\n",
       "0               109             45                 47                   3   \n",
       "1               109             45                 47                   3   \n",
       "2               109             45                 47                   3   \n",
       "3               109             45                 47                   3   \n",
       "4                20             17                 16                   2   \n",
       "\n",
       "   time_stamp_std  \n",
       "0      206.449449  \n",
       "1      206.449449  \n",
       "2      206.449449  \n",
       "3      206.449449  \n",
       "4      218.341897  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#活跃天数方差\n",
    "std=user_log.groupby(['user_id'])['time_stamp'].std().reset_index()\n",
    "all_data=all_data.merge(std,on=['user_id'],how='inner')\n",
    "all_data.rename(columns={'time_stamp':'time_stamp_std'},inplace=True)\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "efbfc615-2369-4822-9dca-798561400b32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def most_love(data_1,most_name,new_name_2):\n",
    "    data2=user_log.groupby(['user_id'])[most_name].apply(lambda x:Counter(x).most_common(1)[0][0]).reset_index()\n",
    "    data_union_1=data_1.merge(data2,on=['user_id'],how='inner')\n",
    "    data_union_1.rename(columns={most_name:new_name_2},inplace=True)\n",
    "    return data_union_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3ef329bd-699f-4fe3-b40c-2f87c2a82b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#用户最喜欢的店铺\n",
    "all_data=most_love(all_data,'seller_id','sell_id_most')\n",
    "#最喜欢的类目\n",
    "all_data=most_love(all_data,'cat_id','cat_id_most')\n",
    "#最喜欢的品牌\n",
    "all_data=most_love(all_data,'brand_id','brand_id_most')\n",
    "#最常见的行为动作\n",
    "all_data=most_love(all_data,'action_type','action_type_most')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c831b2aa-175a-463e-9e72-16efe2318cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>action_type_unique</th>\n",
       "      <th>time_stamp_std</th>\n",
       "      <th>sell_id_most</th>\n",
       "      <th>cat_id_most</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "      <td>3556</td>\n",
       "      <td>407</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514762</th>\n",
       "      <td>228479</td>\n",
       "      <td>3111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>278</td>\n",
       "      <td>71</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>156.030774</td>\n",
       "      <td>3017</td>\n",
       "      <td>662</td>\n",
       "      <td>7703.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514763</th>\n",
       "      <td>97919</td>\n",
       "      <td>2341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>196.860259</td>\n",
       "      <td>235</td>\n",
       "      <td>464</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514764</th>\n",
       "      <td>97919</td>\n",
       "      <td>3971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>196.860259</td>\n",
       "      <td>235</td>\n",
       "      <td>464</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514765</th>\n",
       "      <td>32639</td>\n",
       "      <td>3536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>172.779785</td>\n",
       "      <td>3319</td>\n",
       "      <td>898</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514766</th>\n",
       "      <td>32639</td>\n",
       "      <td>3319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>172.779785</td>\n",
       "      <td>3319</td>\n",
       "      <td>898</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514767 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0       1        6.0     0.0       451   \n",
       "1         34176          121    0.0       1        6.0     0.0       451   \n",
       "2         34176         4356    1.0       1        6.0     0.0       451   \n",
       "3         34176         2217    0.0       1        6.0     0.0       451   \n",
       "4        230784         4818    0.0       1        0.0     0.0        54   \n",
       "...         ...          ...    ...     ...        ...     ...       ...   \n",
       "514762   228479         3111    NaN      -1        6.0     0.0      2004   \n",
       "514763    97919         2341    NaN      -1        8.0     1.0        55   \n",
       "514764    97919         3971    NaN      -1        8.0     1.0        55   \n",
       "514765    32639         3536    NaN      -1        0.0     0.0        72   \n",
       "514766    32639         3319    NaN      -1        0.0     0.0        72   \n",
       "\n",
       "        seller_id_unique  cat_id_unique  time_stamp_unique  \\\n",
       "0                    109             45                 47   \n",
       "1                    109             45                 47   \n",
       "2                    109             45                 47   \n",
       "3                    109             45                 47   \n",
       "4                     20             17                 16   \n",
       "...                  ...            ...                ...   \n",
       "514762               278             71                110   \n",
       "514763                17             14                  8   \n",
       "514764                17             14                  8   \n",
       "514765                33             24                 15   \n",
       "514766                33             24                 15   \n",
       "\n",
       "        action_type_unique  time_stamp_std  sell_id_most  cat_id_most  \\\n",
       "0                        3      206.449449           331          662   \n",
       "1                        3      206.449449           331          662   \n",
       "2                        3      206.449449           331          662   \n",
       "3                        3      206.449449           331          662   \n",
       "4                        2      218.341897          3556          407   \n",
       "...                    ...             ...           ...          ...   \n",
       "514762                   3      156.030774          3017          662   \n",
       "514763                   3      196.860259           235          464   \n",
       "514764                   3      196.860259           235          464   \n",
       "514765                   4      172.779785          3319          898   \n",
       "514766                   4      172.779785          3319          898   \n",
       "\n",
       "        brand_id_most  action_type_most  \n",
       "0              4094.0                 0  \n",
       "1              4094.0                 0  \n",
       "2              4094.0                 0  \n",
       "3              4094.0                 0  \n",
       "4              1236.0                 0  \n",
       "...               ...               ...  \n",
       "514762         7703.0                 0  \n",
       "514763         2020.0                 0  \n",
       "514764         2020.0                 0  \n",
       "514765         5508.0                 0  \n",
       "514766         5508.0                 0  \n",
       "\n",
       "[514767 rows x 16 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "261e259a-3b75-4915-ac62-498cdaa18f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def most_love_cnt(data_1,most_name,new_name_2):\n",
    "    data2=user_log.groupby(['user_id'])[most_name].apply(lambda x:Counter(x).most_common(1)[0][1]).reset_index()\n",
    "    data_union_1=data_1.merge(data2,on=['user_id'],how='inner')\n",
    "    data_union_1.rename(columns={most_name:new_name_2},inplace=True)\n",
    "    return data_union_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e914e6cc-f14b-4083-8459-0d26558d4bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#用户最喜欢的店铺 行为次数\n",
    "all_data=most_love_cnt(all_data,'seller_id','sell_id_most_cnt')\n",
    "#最喜欢的类目 行为次数\n",
    "all_data=most_love_cnt(all_data,'cat_id','cat_id_most_cnt')\n",
    "#最喜欢的品牌 行为次数\n",
    "all_data=most_love_cnt(all_data,'brand_id','brand_id_most_cnt')\n",
    "#最常见的行为动作 行为次数\n",
    "all_data=most_love_cnt(all_data,'action_type','action_type_most_cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "356247af-87f8-4ce6-a368-abb19f886bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>action_type_unique</th>\n",
       "      <th>time_stamp_std</th>\n",
       "      <th>sell_id_most</th>\n",
       "      <th>cat_id_most</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>sell_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "      <td>3556</td>\n",
       "      <td>407</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514762</th>\n",
       "      <td>228479</td>\n",
       "      <td>3111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>278</td>\n",
       "      <td>71</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>156.030774</td>\n",
       "      <td>3017</td>\n",
       "      <td>662</td>\n",
       "      <td>7703.0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>284</td>\n",
       "      <td>147</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514763</th>\n",
       "      <td>97919</td>\n",
       "      <td>2341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>196.860259</td>\n",
       "      <td>235</td>\n",
       "      <td>464</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514764</th>\n",
       "      <td>97919</td>\n",
       "      <td>3971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>196.860259</td>\n",
       "      <td>235</td>\n",
       "      <td>464</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514765</th>\n",
       "      <td>32639</td>\n",
       "      <td>3536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>172.779785</td>\n",
       "      <td>3319</td>\n",
       "      <td>898</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514766</th>\n",
       "      <td>32639</td>\n",
       "      <td>3319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>172.779785</td>\n",
       "      <td>3319</td>\n",
       "      <td>898</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514767 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0       1        6.0     0.0       451   \n",
       "1         34176          121    0.0       1        6.0     0.0       451   \n",
       "2         34176         4356    1.0       1        6.0     0.0       451   \n",
       "3         34176         2217    0.0       1        6.0     0.0       451   \n",
       "4        230784         4818    0.0       1        0.0     0.0        54   \n",
       "...         ...          ...    ...     ...        ...     ...       ...   \n",
       "514762   228479         3111    NaN      -1        6.0     0.0      2004   \n",
       "514763    97919         2341    NaN      -1        8.0     1.0        55   \n",
       "514764    97919         3971    NaN      -1        8.0     1.0        55   \n",
       "514765    32639         3536    NaN      -1        0.0     0.0        72   \n",
       "514766    32639         3319    NaN      -1        0.0     0.0        72   \n",
       "\n",
       "        seller_id_unique  cat_id_unique  time_stamp_unique  \\\n",
       "0                    109             45                 47   \n",
       "1                    109             45                 47   \n",
       "2                    109             45                 47   \n",
       "3                    109             45                 47   \n",
       "4                     20             17                 16   \n",
       "...                  ...            ...                ...   \n",
       "514762               278             71                110   \n",
       "514763                17             14                  8   \n",
       "514764                17             14                  8   \n",
       "514765                33             24                 15   \n",
       "514766                33             24                 15   \n",
       "\n",
       "        action_type_unique  time_stamp_std  sell_id_most  cat_id_most  \\\n",
       "0                        3      206.449449           331          662   \n",
       "1                        3      206.449449           331          662   \n",
       "2                        3      206.449449           331          662   \n",
       "3                        3      206.449449           331          662   \n",
       "4                        2      218.341897          3556          407   \n",
       "...                    ...             ...           ...          ...   \n",
       "514762                   3      156.030774          3017          662   \n",
       "514763                   3      196.860259           235          464   \n",
       "514764                   3      196.860259           235          464   \n",
       "514765                   4      172.779785          3319          898   \n",
       "514766                   4      172.779785          3319          898   \n",
       "\n",
       "        brand_id_most  action_type_most  sell_id_most_cnt  cat_id_most_cnt  \\\n",
       "0              4094.0                 0                70               98   \n",
       "1              4094.0                 0                70               98   \n",
       "2              4094.0                 0                70               98   \n",
       "3              4094.0                 0                70               98   \n",
       "4              1236.0                 0                10                9   \n",
       "...               ...               ...               ...              ...   \n",
       "514762         7703.0                 0               147              284   \n",
       "514763         2020.0                 0                18               20   \n",
       "514764         2020.0                 0                18               20   \n",
       "514765         5508.0                 0                11               12   \n",
       "514766         5508.0                 0                11               12   \n",
       "\n",
       "        brand_id_most_cnt  action_type_most_cnt  \n",
       "0                      70                   410  \n",
       "1                      70                   410  \n",
       "2                      70                   410  \n",
       "3                      70                   410  \n",
       "4                      10                    47  \n",
       "...                   ...                   ...  \n",
       "514762                147                  1770  \n",
       "514763                 18                    46  \n",
       "514764                 18                    46  \n",
       "514765                 12                    62  \n",
       "514766                 12                    62  \n",
       "\n",
       "[514767 rows x 20 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "766f0fe5-6552-4dae-a3e8-0e21be4048fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_id_union=list(set(all_data['user_id']))\n",
    "def action_type_select(data,num,new_name):\n",
    "    d=user_log.groupby(['user_id'])['action_type'].apply(lambda x:Counter(x))\n",
    "    e=dict(d)\n",
    "    k=[]\n",
    "    for i in user_id_union:\n",
    "        try:\n",
    "            k.append(e[(i,num)])\n",
    "        except KeyError:\n",
    "            k.append(0)\n",
    "    data3=pd.DataFrame({'user_id':user_id_union,new_name:k})\n",
    "    data_union_2=data.merge(data3,on=['user_id'],how='inner')\n",
    "    return data_union_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a804912c-ba73-408c-8088-1fc3c5e5844f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#点击次数\n",
    "all_data=action_type_select(all_data,0,'action_type_sum_0')\n",
    "#加购次数\n",
    "all_data=action_type_select(all_data,1,'action_type_sum_1')\n",
    "#购买次数\n",
    "all_data=action_type_select(all_data,2,'action_type_sum_2')\n",
    "#收藏次数\n",
    "all_data=action_type_select(all_data,3,'action_type_sum_3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4966505f-5214-401f-a451-17977ca442c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>sell_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2</th>\n",
       "      <th>action_type_sum_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_id_unique  cat_id_unique  time_stamp_unique  ...  brand_id_most  \\\n",
       "0               109             45                 47  ...         4094.0   \n",
       "1               109             45                 47  ...         4094.0   \n",
       "2               109             45                 47  ...         4094.0   \n",
       "3               109             45                 47  ...         4094.0   \n",
       "4                20             17                 16  ...         1236.0   \n",
       "\n",
       "   action_type_most  sell_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                 0                70               98                 70   \n",
       "1                 0                70               98                 70   \n",
       "2                 0                70               98                 70   \n",
       "3                 0                70               98                 70   \n",
       "4                 0                10                9                 10   \n",
       "\n",
       "   action_type_most_cnt  action_type_sum_0  action_type_sum_1  \\\n",
       "0                   410              410.0                NaN   \n",
       "1                   410              410.0                NaN   \n",
       "2                   410              410.0                NaN   \n",
       "3                   410              410.0                NaN   \n",
       "4                    47               47.0                NaN   \n",
       "\n",
       "   action_type_sum_2  action_type_sum_3  \n",
       "0               34.0                7.0  \n",
       "1               34.0                7.0  \n",
       "2               34.0                7.0  \n",
       "3               34.0                7.0  \n",
       "4                7.0                NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f992b611-78f8-4eff-90ad-653494798d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>action_type_unique</th>\n",
       "      <th>time_stamp_std</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>sell_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2</th>\n",
       "      <th>action_type_sum_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163968</td>\n",
       "      <td>4605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>221.073334</td>\n",
       "      <td>...</td>\n",
       "      <td>7178.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>360576</td>\n",
       "      <td>1581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>211.273897</td>\n",
       "      <td>...</td>\n",
       "      <td>4066.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>71</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98688</td>\n",
       "      <td>1964</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>152.015631</td>\n",
       "      <td>...</td>\n",
       "      <td>3637.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98688</td>\n",
       "      <td>3645</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>152.015631</td>\n",
       "      <td>...</td>\n",
       "      <td>3637.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>295296</td>\n",
       "      <td>3361</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>176</td>\n",
       "      <td>56</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>147.967514</td>\n",
       "      <td>...</td>\n",
       "      <td>487.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>59</td>\n",
       "      <td>49</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  merchant_id  age_range  gender  sell_sum  seller_id_unique  \\\n",
       "0   163968         4605        0.0     0.0        81                21   \n",
       "1   360576         1581        2.0     2.0        77                37   \n",
       "2    98688         1964        6.0     0.0        56                22   \n",
       "3    98688         3645        6.0     0.0        56                22   \n",
       "4   295296         3361        2.0     1.0       176                56   \n",
       "\n",
       "   cat_id_unique  time_stamp_unique  action_type_unique  time_stamp_std  ...  \\\n",
       "0             21                 26                   3      221.073334  ...   \n",
       "1             27                 22                   2      211.273897  ...   \n",
       "2             18                 10                   2      152.015631  ...   \n",
       "3             18                 10                   2      152.015631  ...   \n",
       "4             32                 33                   3      147.967514  ...   \n",
       "\n",
       "   brand_id_most  action_type_most  sell_id_most_cnt  cat_id_most_cnt  \\\n",
       "0         7178.0                 0                22               17   \n",
       "1         4066.0                 0                10               13   \n",
       "2         3637.0                 0                11               11   \n",
       "3         3637.0                 0                11               11   \n",
       "4          487.0                 0                50               59   \n",
       "\n",
       "   brand_id_most_cnt  action_type_most_cnt  action_type_sum_0  \\\n",
       "0                 22                    63               63.0   \n",
       "1                 10                    71               71.0   \n",
       "2                 11                    51               51.0   \n",
       "3                 11                    51               51.0   \n",
       "4                 49                   162              162.0   \n",
       "\n",
       "   action_type_sum_1  action_type_sum_2  action_type_sum_3  \n",
       "0                0.0               16.0                2.0  \n",
       "1                0.0                6.0                0.0  \n",
       "2                0.0                5.0                0.0  \n",
       "3                0.0                5.0                0.0  \n",
       "4                0.0                7.0                7.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=all_data[all_data['target']==1].reset_index(drop=True)\n",
    "test=all_data[all_data['target']==-1].reset_index(drop=True)\n",
    "train.drop(['target'],axis=1,inplace=True)\n",
    "test.drop(['target'],axis=1,inplace=True)\n",
    "train.fillna(0,inplace=True)\n",
    "test.drop(['label'],axis=1,inplace=True)\n",
    "test.fillna(0,inplace=True)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dc11a874-1a05-4fb5-8b20-04ce236d197f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\train_all_k.csv',header=True,index=False)\n",
    "test.to_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\test_all_k.csv',header=True,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "565ae5b9-295c-43aa-887d-b08ecf8b55bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0.0    241303\n",
       "1.0     15838\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b8d2d504-ffd7-45c3-b1dc-57af34e36a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "11728e9a-0dbd-4162-87ae-eb396cc2fea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\train_all_k.csv')\n",
    "test_data=pd.read_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\test_all_k.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7ad0978e-a66b-4eac-8874-79ebc2a7844d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(257141, 23)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "15cfa805-ae19-4352-988b-c8e23693c89d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(257626, 22)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0a23b621-9e85-4cd0-bbf3-19e9d64d5546",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score,roc_curve,accuracy_score,confusion_matrix,log_loss,auc,precision_recall_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2ff9206c-bd13-4859-bc4a-36e44ff8409d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_clf(model):\n",
    "    model.fit(X_train, y_train) #使用训练数据拟合模型\n",
    "    y_train_pred = model.predict_proba(X_train)\n",
    "    y_train_pred_pos = y_train_pred[:, 1]\n",
    "    \n",
    "    y_test_pred = model.predict_proba(X_test)\n",
    "    y_test_pred_pos = y_test_pred[:,1]\n",
    "\n",
    "    auc_train=roc_auc_score(y_train,y_train_pred_pos)#AUC评分\n",
    "    auc_test =roc_auc_score(y_test, y_test_pred_pos)\n",
    "\n",
    "    print(f\"Train AUC Score {auc_train}\")\n",
    "    print(f\"Test AUC Score {auc_test}\")\n",
    "    \n",
    "    fpr,tpr,_=roc_curve(y_test,y_test_pred_pos)#绘制ROC曲线\n",
    "    return fpr,tpr # 返回假正率和真正率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f6400221-1f9c-44a0-8dfb-146c0b580744",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_1=train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b62c470e-ea82-4f8b-9242-7f02c04cfdb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_id_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>action_type_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>sell_id_most_cnt</th>\n",
       "      <th>cat_id_most_cnt</th>\n",
       "      <th>brand_id_most_cnt</th>\n",
       "      <th>action_type_most_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2</th>\n",
       "      <th>action_type_sum_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257136</th>\n",
       "      <td>359807</td>\n",
       "      <td>4325</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>107</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257137</th>\n",
       "      <td>294527</td>\n",
       "      <td>3971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>198</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257138</th>\n",
       "      <td>294527</td>\n",
       "      <td>152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>198</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257139</th>\n",
       "      <td>294527</td>\n",
       "      <td>2537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>198</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>162</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257140</th>\n",
       "      <td>229247</td>\n",
       "      <td>4140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>194</td>\n",
       "      <td>50</td>\n",
       "      <td>29</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5697.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>181</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>257141 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  merchant_id  label  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0        6.0     0.0       451   \n",
       "1         34176          121    0.0        6.0     0.0       451   \n",
       "2         34176         4356    1.0        6.0     0.0       451   \n",
       "3         34176         2217    0.0        6.0     0.0       451   \n",
       "4        230784         4818    0.0        0.0     0.0        54   \n",
       "...         ...          ...    ...        ...     ...       ...   \n",
       "257136   359807         4325    0.0        4.0     1.0       117   \n",
       "257137   294527         3971    0.0        0.0     1.0       198   \n",
       "257138   294527          152    0.0        0.0     1.0       198   \n",
       "257139   294527         2537    0.0        0.0     1.0       198   \n",
       "257140   229247         4140    0.0        4.0     2.0       194   \n",
       "\n",
       "        seller_id_unique  cat_id_unique  time_stamp_unique  \\\n",
       "0                    109             45                 47   \n",
       "1                    109             45                 47   \n",
       "2                    109             45                 47   \n",
       "3                    109             45                 47   \n",
       "4                     20             17                 16   \n",
       "...                  ...            ...                ...   \n",
       "257136                33             25                 12   \n",
       "257137                38             20                  6   \n",
       "257138                38             20                  6   \n",
       "257139                38             20                  6   \n",
       "257140                50             29                 23   \n",
       "\n",
       "        action_type_unique  ...  brand_id_most  action_type_most  \\\n",
       "0                        3  ...         4094.0                 0   \n",
       "1                        3  ...         4094.0                 0   \n",
       "2                        3  ...         4094.0                 0   \n",
       "3                        3  ...         4094.0                 0   \n",
       "4                        2  ...         1236.0                 0   \n",
       "...                    ...  ...            ...               ...   \n",
       "257136                   3  ...         2276.0                 0   \n",
       "257137                   3  ...         6143.0                 0   \n",
       "257138                   3  ...         6143.0                 0   \n",
       "257139                   3  ...         6143.0                 0   \n",
       "257140                   3  ...         5697.0                 0   \n",
       "\n",
       "        sell_id_most_cnt  cat_id_most_cnt  brand_id_most_cnt  \\\n",
       "0                     70               98                 70   \n",
       "1                     70               98                 70   \n",
       "2                     70               98                 70   \n",
       "3                     70               98                 70   \n",
       "4                     10                9                 10   \n",
       "...                  ...              ...                ...   \n",
       "257136                22               15                 25   \n",
       "257137                28               38                 28   \n",
       "257138                28               38                 28   \n",
       "257139                28               38                 28   \n",
       "257140                24               33                 24   \n",
       "\n",
       "        action_type_most_cnt  action_type_sum_0  action_type_sum_1  \\\n",
       "0                        410              410.0                0.0   \n",
       "1                        410              410.0                0.0   \n",
       "2                        410              410.0                0.0   \n",
       "3                        410              410.0                0.0   \n",
       "4                         47               47.0                0.0   \n",
       "...                      ...                ...                ...   \n",
       "257136                   107              107.0                0.0   \n",
       "257137                   162              162.0                0.0   \n",
       "257138                   162              162.0                0.0   \n",
       "257139                   162              162.0                0.0   \n",
       "257140                   181              181.0                0.0   \n",
       "\n",
       "        action_type_sum_2  action_type_sum_3  \n",
       "0                    34.0                7.0  \n",
       "1                    34.0                7.0  \n",
       "2                    34.0                7.0  \n",
       "3                    34.0                7.0  \n",
       "4                     7.0                0.0  \n",
       "...                   ...                ...  \n",
       "257136                9.0                1.0  \n",
       "257137                5.0               31.0  \n",
       "257138                5.0               31.0  \n",
       "257139                5.0               31.0  \n",
       "257140                9.0                4.0  \n",
       "\n",
       "[257141 rows x 23 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "afffe4db-62b2-4c65-8b97-11219d5392b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用 pandas DataFrame 的 fillna 方法填充 train_data_1 中的缺失值（NaN）为 0\n",
    "train_data_1=train_data_1.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "143fa4a5-da05-4b43-bfaa-0eceb0a64aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "label=train_data_1['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3cc00af1-346d-40d9-a81d-3da3407f94d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_data_1['user_id']\n",
    "del train_data_1['merchant_id']\n",
    "del train_data_1['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "59e8607c-6822-45d9-9c1d-99d1915885d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5839329993989572\n",
      "Test AUC Score 0.579279808507758\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 2.07326934e-05, 8.29307735e-05, ...,\n",
       "        9.99709742e-01, 9.99958535e-01, 1.00000000e+00]),\n",
       " array([0., 0., 0., ..., 1., 1., 1.]))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1.逻辑回归\n",
    "stdScaler = StandardScaler()\n",
    "X = stdScaler.fit_transform(train_data_1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label, test_size=0.2,random_state=9)\n",
    "#将数据划分为训练集和测试集\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial',class_weight='balanced')\n",
    "model_clf(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bdc84a4b-e5ed-4fec-adb0-32eb682dcf3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5783302810281965\n",
      "Test AUC Score 0.5714223249516577\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 1.65510849e-05, 4.96532548e-05, ...,\n",
       "        9.99420712e-01, 9.99735183e-01, 1.00000000e+00]),\n",
       " array([0.       , 0.       , 0.       , ..., 0.9994828, 0.9994828,\n",
       "        1.       ]))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.随机森林分类器\n",
    "X_train, X_test, y_train,y_test = train_test_split(train_data_1, label, random_state=0)\n",
    "clf = RandomForestClassifier(n_estimators=10, max_depth=3, min_samples_split=12, random_state=0)\n",
    "model_clf(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d020bed0-9238-4aef-8c83-370b17c45ac7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.5852768562296854\n",
      "Test AUC Score 0.5769059151030866\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 9.93065095e-05, 1.65510849e-04, 1.15857594e-03,\n",
       "        1.70476175e-03, 8.37484897e-03, 8.82172826e-03, 8.87138152e-03,\n",
       "        8.97068803e-03, 1.06423476e-02, 1.56242242e-02, 4.46879293e-02,\n",
       "        4.50355021e-02, 4.60285672e-02, 4.60616693e-02, 4.61775269e-02,\n",
       "        6.89683709e-02, 7.05407239e-02, 7.49102104e-02, 7.55888048e-02,\n",
       "        7.56715603e-02, 7.62673993e-02, 7.96934739e-02, 8.60490905e-02,\n",
       "        8.60656416e-02, 1.27211639e-01, 1.35272017e-01, 1.37986395e-01,\n",
       "        1.38631887e-01, 1.38664989e-01, 1.39442890e-01, 1.39624952e-01,\n",
       "        1.46394346e-01, 1.61786855e-01, 1.78420695e-01, 1.86001092e-01,\n",
       "        1.86116950e-01, 1.90817458e-01, 2.08361608e-01, 2.08874692e-01,\n",
       "        2.09685695e-01, 2.10927026e-01, 2.11158741e-01, 2.12102153e-01,\n",
       "        2.12300766e-01, 2.12466277e-01, 2.12466277e-01, 2.65727668e-01,\n",
       "        2.83784902e-01, 3.03364836e-01, 3.07635015e-01, 3.12021053e-01,\n",
       "        3.15364372e-01, 3.15480230e-01, 3.20677270e-01, 3.23010973e-01,\n",
       "        3.52256740e-01, 3.52273292e-01, 3.52869131e-01, 3.76901306e-01,\n",
       "        3.76934408e-01, 3.77315083e-01, 4.29930982e-01, 4.41086413e-01,\n",
       "        4.41582946e-01, 4.52440457e-01, 4.55568613e-01, 4.56296860e-01,\n",
       "        4.64539301e-01, 4.66757146e-01, 5.28476142e-01, 5.86669756e-01,\n",
       "        6.33575531e-01, 6.75416674e-01, 6.76724209e-01, 6.90378854e-01,\n",
       "        6.93904235e-01, 6.95393833e-01, 6.95426935e-01, 6.97826843e-01,\n",
       "        7.34984028e-01, 7.62210563e-01, 7.63468445e-01, 7.85117264e-01,\n",
       "        8.05806121e-01, 8.60044026e-01, 9.52548040e-01, 9.52647346e-01,\n",
       "        9.52829408e-01, 9.55113458e-01, 9.55427928e-01, 9.60360152e-01,\n",
       "        9.64928251e-01, 9.77341565e-01, 9.96789090e-01, 9.97004254e-01,\n",
       "        9.97566991e-01, 9.99983449e-01, 1.00000000e+00]),\n",
       " array([0.00000000e+00, 5.17196793e-04, 7.75795190e-04, 4.39617274e-03,\n",
       "        5.68916473e-03, 1.75846910e-02, 1.81018878e-02, 1.81018878e-02,\n",
       "        1.81018878e-02, 2.06878717e-02, 3.36177916e-02, 7.73209206e-02,\n",
       "        7.73209206e-02, 8.04241014e-02, 8.06826998e-02, 8.19756918e-02,\n",
       "        1.11973106e-01, 1.13783295e-01, 1.20765451e-01, 1.22058443e-01,\n",
       "        1.22058443e-01, 1.24127230e-01, 1.29557797e-01, 1.40160331e-01,\n",
       "        1.40160331e-01, 1.89552625e-01, 2.02999741e-01, 2.05327127e-01,\n",
       "        2.06361521e-01, 2.06878717e-01, 2.07913111e-01, 2.08947505e-01,\n",
       "        2.18515645e-01, 2.37651927e-01, 2.60925782e-01, 2.67132144e-01,\n",
       "        2.67649341e-01, 2.74114300e-01, 2.94543574e-01, 2.95060771e-01,\n",
       "        2.95577967e-01, 2.98422550e-01, 2.98422550e-01, 2.99198345e-01,\n",
       "        2.99715542e-01, 2.99715542e-01, 2.99974140e-01, 3.62296354e-01,\n",
       "        3.81949832e-01, 4.02637704e-01, 4.07292475e-01, 4.14791828e-01,\n",
       "        4.15826222e-01, 4.16084820e-01, 4.22808379e-01, 4.25135764e-01,\n",
       "        4.61339540e-01, 4.61339540e-01, 4.62373933e-01, 4.85906387e-01,\n",
       "        4.85906387e-01, 4.86164986e-01, 5.44866822e-01, 5.55986553e-01,\n",
       "        5.55986553e-01, 5.66330489e-01, 5.69175071e-01, 5.69433670e-01,\n",
       "        5.78743212e-01, 5.79519007e-01, 6.35893457e-01, 6.90716318e-01,\n",
       "        7.32609258e-01, 7.67520041e-01, 7.67778640e-01, 7.79674166e-01,\n",
       "        7.81742953e-01, 7.82260150e-01, 7.82260150e-01, 7.84846134e-01,\n",
       "        8.15102146e-01, 8.34238428e-01, 8.35272821e-01, 8.56736488e-01,\n",
       "        8.67856219e-01, 9.10783553e-01, 9.73105767e-01, 9.73105767e-01,\n",
       "        9.73105767e-01, 9.74140160e-01, 9.74398759e-01, 9.77243341e-01,\n",
       "        9.79829325e-01, 9.86811482e-01, 9.98448410e-01, 9.98965606e-01,\n",
       "        9.99224205e-01, 1.00000000e+00, 1.00000000e+00]))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3.梯度提升\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data_1, label, random_state=0)\n",
    "clf = GradientBoostingClassifier(n_estimators=10, learning_rate=1.0, max_depth=1, random_state=0)\n",
    "model_clf(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "80747bb8-3b3d-4339-8518-e636a65d85e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mlogloss:0.67057\teval-mlogloss:0.67051\n",
      "[1]\ttrain-mlogloss:0.64930\teval-mlogloss:0.64918\n",
      "[2]\ttrain-mlogloss:0.62924\teval-mlogloss:0.62907\n",
      "[3]\ttrain-mlogloss:0.61028\teval-mlogloss:0.61006\n",
      "[4]\ttrain-mlogloss:0.59236\teval-mlogloss:0.59208\n",
      "[5]\ttrain-mlogloss:0.57542\teval-mlogloss:0.57509\n",
      "[6]\ttrain-mlogloss:0.55936\teval-mlogloss:0.55898\n",
      "[7]\ttrain-mlogloss:0.54415\teval-mlogloss:0.54373\n",
      "[8]\ttrain-mlogloss:0.52972\teval-mlogloss:0.52925\n",
      "[9]\ttrain-mlogloss:0.51603\teval-mlogloss:0.51551\n",
      "[10]\ttrain-mlogloss:0.50303\teval-mlogloss:0.50247\n",
      "[11]\ttrain-mlogloss:0.49069\teval-mlogloss:0.49008\n",
      "[12]\ttrain-mlogloss:0.47890\teval-mlogloss:0.47826\n",
      "[13]\ttrain-mlogloss:0.46771\teval-mlogloss:0.46702\n",
      "[14]\ttrain-mlogloss:0.45705\teval-mlogloss:0.45632\n",
      "[15]\ttrain-mlogloss:0.44691\teval-mlogloss:0.44615\n",
      "[16]\ttrain-mlogloss:0.43726\teval-mlogloss:0.43649\n",
      "[17]\ttrain-mlogloss:0.42806\teval-mlogloss:0.42726\n",
      "[18]\ttrain-mlogloss:0.41926\teval-mlogloss:0.41844\n",
      "[19]\ttrain-mlogloss:0.41088\teval-mlogloss:0.41004\n",
      "[20]\ttrain-mlogloss:0.40288\teval-mlogloss:0.40202\n",
      "[21]\ttrain-mlogloss:0.39524\teval-mlogloss:0.39437\n",
      "[22]\ttrain-mlogloss:0.38794\teval-mlogloss:0.38706\n",
      "[23]\ttrain-mlogloss:0.38099\teval-mlogloss:0.38009\n",
      "[24]\ttrain-mlogloss:0.37433\teval-mlogloss:0.37342\n",
      "[25]\ttrain-mlogloss:0.36797\teval-mlogloss:0.36705\n",
      "[26]\ttrain-mlogloss:0.36188\teval-mlogloss:0.36095\n",
      "[27]\ttrain-mlogloss:0.35608\teval-mlogloss:0.35513\n",
      "[28]\ttrain-mlogloss:0.35053\teval-mlogloss:0.34958\n",
      "[29]\ttrain-mlogloss:0.34523\teval-mlogloss:0.34426\n",
      "[30]\ttrain-mlogloss:0.34015\teval-mlogloss:0.33918\n",
      "[31]\ttrain-mlogloss:0.33530\teval-mlogloss:0.33432\n",
      "[32]\ttrain-mlogloss:0.33066\teval-mlogloss:0.32966\n",
      "[33]\ttrain-mlogloss:0.32620\teval-mlogloss:0.32521\n",
      "[34]\ttrain-mlogloss:0.32196\teval-mlogloss:0.32096\n",
      "[35]\ttrain-mlogloss:0.31790\teval-mlogloss:0.31690\n",
      "[36]\ttrain-mlogloss:0.31403\teval-mlogloss:0.31303\n",
      "[37]\ttrain-mlogloss:0.31032\teval-mlogloss:0.30933\n",
      "[38]\ttrain-mlogloss:0.30677\teval-mlogloss:0.30577\n",
      "[39]\ttrain-mlogloss:0.30336\teval-mlogloss:0.30236\n",
      "[40]\ttrain-mlogloss:0.30011\teval-mlogloss:0.29911\n",
      "[41]\ttrain-mlogloss:0.29700\teval-mlogloss:0.29601\n",
      "[42]\ttrain-mlogloss:0.29403\teval-mlogloss:0.29304\n",
      "[43]\ttrain-mlogloss:0.29118\teval-mlogloss:0.29020\n",
      "[44]\ttrain-mlogloss:0.28846\teval-mlogloss:0.28748\n",
      "[45]\ttrain-mlogloss:0.28585\teval-mlogloss:0.28488\n",
      "[46]\ttrain-mlogloss:0.28335\teval-mlogloss:0.28239\n",
      "[47]\ttrain-mlogloss:0.28096\teval-mlogloss:0.28000\n",
      "[48]\ttrain-mlogloss:0.27868\teval-mlogloss:0.27773\n",
      "[49]\ttrain-mlogloss:0.27649\teval-mlogloss:0.27555\n",
      "[50]\ttrain-mlogloss:0.27440\teval-mlogloss:0.27347\n",
      "[51]\ttrain-mlogloss:0.27240\teval-mlogloss:0.27148\n",
      "[52]\ttrain-mlogloss:0.27051\teval-mlogloss:0.26959\n",
      "[53]\ttrain-mlogloss:0.26869\teval-mlogloss:0.26780\n",
      "[54]\ttrain-mlogloss:0.26694\teval-mlogloss:0.26606\n",
      "[55]\ttrain-mlogloss:0.26527\teval-mlogloss:0.26441\n",
      "[56]\ttrain-mlogloss:0.26368\teval-mlogloss:0.26283\n",
      "[57]\ttrain-mlogloss:0.26215\teval-mlogloss:0.26133\n",
      "[58]\ttrain-mlogloss:0.26070\teval-mlogloss:0.25989\n",
      "[59]\ttrain-mlogloss:0.25930\teval-mlogloss:0.25851\n",
      "[60]\ttrain-mlogloss:0.25797\teval-mlogloss:0.25720\n",
      "[61]\ttrain-mlogloss:0.25670\teval-mlogloss:0.25595\n",
      "[62]\ttrain-mlogloss:0.25549\teval-mlogloss:0.25476\n",
      "[63]\ttrain-mlogloss:0.25433\teval-mlogloss:0.25362\n",
      "[64]\ttrain-mlogloss:0.25322\teval-mlogloss:0.25253\n",
      "[65]\ttrain-mlogloss:0.25216\teval-mlogloss:0.25149\n",
      "[66]\ttrain-mlogloss:0.25116\teval-mlogloss:0.25052\n",
      "[67]\ttrain-mlogloss:0.25019\teval-mlogloss:0.24958\n",
      "[68]\ttrain-mlogloss:0.24928\teval-mlogloss:0.24869\n",
      "[69]\ttrain-mlogloss:0.24839\teval-mlogloss:0.24784\n",
      "[70]\ttrain-mlogloss:0.24756\teval-mlogloss:0.24703\n",
      "[71]\ttrain-mlogloss:0.24675\teval-mlogloss:0.24625\n",
      "[72]\ttrain-mlogloss:0.24599\teval-mlogloss:0.24552\n",
      "[73]\ttrain-mlogloss:0.24526\teval-mlogloss:0.24483\n",
      "[74]\ttrain-mlogloss:0.24457\teval-mlogloss:0.24417\n",
      "[75]\ttrain-mlogloss:0.24391\teval-mlogloss:0.24354\n",
      "[76]\ttrain-mlogloss:0.24328\teval-mlogloss:0.24294\n",
      "[77]\ttrain-mlogloss:0.24268\teval-mlogloss:0.24237\n",
      "[78]\ttrain-mlogloss:0.24209\teval-mlogloss:0.24182\n",
      "[79]\ttrain-mlogloss:0.24154\teval-mlogloss:0.24130\n",
      "[80]\ttrain-mlogloss:0.24101\teval-mlogloss:0.24081\n",
      "[81]\ttrain-mlogloss:0.24050\teval-mlogloss:0.24034\n",
      "[82]\ttrain-mlogloss:0.24002\teval-mlogloss:0.23990\n",
      "[83]\ttrain-mlogloss:0.23956\teval-mlogloss:0.23948\n",
      "[84]\ttrain-mlogloss:0.23912\teval-mlogloss:0.23908\n",
      "[85]\ttrain-mlogloss:0.23870\teval-mlogloss:0.23870\n",
      "[86]\ttrain-mlogloss:0.23830\teval-mlogloss:0.23833\n",
      "[87]\ttrain-mlogloss:0.23792\teval-mlogloss:0.23800\n",
      "[88]\ttrain-mlogloss:0.23756\teval-mlogloss:0.23768\n",
      "[89]\ttrain-mlogloss:0.23721\teval-mlogloss:0.23737\n",
      "[90]\ttrain-mlogloss:0.23688\teval-mlogloss:0.23707\n",
      "[91]\ttrain-mlogloss:0.23656\teval-mlogloss:0.23679\n",
      "[92]\ttrain-mlogloss:0.23625\teval-mlogloss:0.23652\n",
      "[93]\ttrain-mlogloss:0.23597\teval-mlogloss:0.23627\n",
      "[94]\ttrain-mlogloss:0.23569\teval-mlogloss:0.23603\n",
      "[95]\ttrain-mlogloss:0.23542\teval-mlogloss:0.23581\n",
      "[96]\ttrain-mlogloss:0.23517\teval-mlogloss:0.23561\n",
      "[97]\ttrain-mlogloss:0.23493\teval-mlogloss:0.23541\n",
      "[98]\ttrain-mlogloss:0.23470\teval-mlogloss:0.23523\n",
      "[99]\ttrain-mlogloss:0.23448\teval-mlogloss:0.23506\n",
      "[100]\ttrain-mlogloss:0.23427\teval-mlogloss:0.23488\n",
      "[101]\ttrain-mlogloss:0.23407\teval-mlogloss:0.23472\n",
      "[102]\ttrain-mlogloss:0.23388\teval-mlogloss:0.23457\n",
      "[103]\ttrain-mlogloss:0.23369\teval-mlogloss:0.23442\n",
      "[104]\ttrain-mlogloss:0.23351\teval-mlogloss:0.23429\n",
      "[105]\ttrain-mlogloss:0.23334\teval-mlogloss:0.23415\n",
      "[106]\ttrain-mlogloss:0.23318\teval-mlogloss:0.23404\n",
      "[107]\ttrain-mlogloss:0.23303\teval-mlogloss:0.23393\n",
      "[108]\ttrain-mlogloss:0.23288\teval-mlogloss:0.23382\n",
      "[109]\ttrain-mlogloss:0.23274\teval-mlogloss:0.23372\n",
      "[110]\ttrain-mlogloss:0.23260\teval-mlogloss:0.23363\n",
      "[111]\ttrain-mlogloss:0.23247\teval-mlogloss:0.23353\n",
      "[112]\ttrain-mlogloss:0.23234\teval-mlogloss:0.23344\n",
      "[113]\ttrain-mlogloss:0.23222\teval-mlogloss:0.23336\n",
      "[114]\ttrain-mlogloss:0.23211\teval-mlogloss:0.23328\n",
      "[115]\ttrain-mlogloss:0.23200\teval-mlogloss:0.23320\n",
      "[116]\ttrain-mlogloss:0.23189\teval-mlogloss:0.23314\n",
      "[117]\ttrain-mlogloss:0.23178\teval-mlogloss:0.23307\n",
      "[118]\ttrain-mlogloss:0.23169\teval-mlogloss:0.23302\n",
      "[119]\ttrain-mlogloss:0.23159\teval-mlogloss:0.23297\n",
      "[120]\ttrain-mlogloss:0.23149\teval-mlogloss:0.23292\n",
      "[121]\ttrain-mlogloss:0.23140\teval-mlogloss:0.23287\n",
      "[122]\ttrain-mlogloss:0.23132\teval-mlogloss:0.23283\n",
      "[123]\ttrain-mlogloss:0.23124\teval-mlogloss:0.23278\n",
      "[124]\ttrain-mlogloss:0.23116\teval-mlogloss:0.23274\n",
      "[125]\ttrain-mlogloss:0.23109\teval-mlogloss:0.23271\n",
      "[126]\ttrain-mlogloss:0.23101\teval-mlogloss:0.23268\n",
      "[127]\ttrain-mlogloss:0.23094\teval-mlogloss:0.23265\n",
      "[128]\ttrain-mlogloss:0.23087\teval-mlogloss:0.23262\n",
      "[129]\ttrain-mlogloss:0.23081\teval-mlogloss:0.23259\n",
      "[130]\ttrain-mlogloss:0.23075\teval-mlogloss:0.23257\n",
      "[131]\ttrain-mlogloss:0.23068\teval-mlogloss:0.23254\n",
      "[132]\ttrain-mlogloss:0.23062\teval-mlogloss:0.23252\n",
      "[133]\ttrain-mlogloss:0.23057\teval-mlogloss:0.23250\n",
      "[134]\ttrain-mlogloss:0.23051\teval-mlogloss:0.23248\n",
      "[135]\ttrain-mlogloss:0.23046\teval-mlogloss:0.23246\n",
      "[136]\ttrain-mlogloss:0.23040\teval-mlogloss:0.23245\n",
      "[137]\ttrain-mlogloss:0.23035\teval-mlogloss:0.23244\n",
      "[138]\ttrain-mlogloss:0.23030\teval-mlogloss:0.23243\n",
      "[139]\ttrain-mlogloss:0.23025\teval-mlogloss:0.23241\n",
      "[140]\ttrain-mlogloss:0.23021\teval-mlogloss:0.23240\n",
      "[141]\ttrain-mlogloss:0.23017\teval-mlogloss:0.23239\n",
      "[142]\ttrain-mlogloss:0.23013\teval-mlogloss:0.23239\n",
      "[143]\ttrain-mlogloss:0.23008\teval-mlogloss:0.23238\n",
      "[144]\ttrain-mlogloss:0.23004\teval-mlogloss:0.23237\n",
      "[145]\ttrain-mlogloss:0.23000\teval-mlogloss:0.23236\n",
      "[146]\ttrain-mlogloss:0.22996\teval-mlogloss:0.23235\n",
      "[147]\ttrain-mlogloss:0.22992\teval-mlogloss:0.23235\n",
      "[148]\ttrain-mlogloss:0.22988\teval-mlogloss:0.23234\n",
      "[149]\ttrain-mlogloss:0.22984\teval-mlogloss:0.23234\n",
      "[150]\ttrain-mlogloss:0.22981\teval-mlogloss:0.23234\n",
      "[151]\ttrain-mlogloss:0.22977\teval-mlogloss:0.23233\n",
      "[152]\ttrain-mlogloss:0.22974\teval-mlogloss:0.23233\n",
      "[153]\ttrain-mlogloss:0.22971\teval-mlogloss:0.23233\n",
      "[154]\ttrain-mlogloss:0.22968\teval-mlogloss:0.23233\n",
      "[155]\ttrain-mlogloss:0.22964\teval-mlogloss:0.23233\n",
      "[156]\ttrain-mlogloss:0.22961\teval-mlogloss:0.23233\n",
      "[157]\ttrain-mlogloss:0.22959\teval-mlogloss:0.23233\n",
      "[158]\ttrain-mlogloss:0.22956\teval-mlogloss:0.23233\n",
      "[159]\ttrain-mlogloss:0.22953\teval-mlogloss:0.23233\n",
      "[160]\ttrain-mlogloss:0.22950\teval-mlogloss:0.23232\n",
      "[161]\ttrain-mlogloss:0.22947\teval-mlogloss:0.23233\n",
      "[162]\ttrain-mlogloss:0.22945\teval-mlogloss:0.23233\n",
      "[163]\ttrain-mlogloss:0.22942\teval-mlogloss:0.23234\n",
      "[164]\ttrain-mlogloss:0.22939\teval-mlogloss:0.23234\n",
      "[165]\ttrain-mlogloss:0.22937\teval-mlogloss:0.23234\n",
      "[166]\ttrain-mlogloss:0.22934\teval-mlogloss:0.23234\n",
      "[167]\ttrain-mlogloss:0.22932\teval-mlogloss:0.23234\n",
      "[168]\ttrain-mlogloss:0.22930\teval-mlogloss:0.23234\n",
      "[169]\ttrain-mlogloss:0.22928\teval-mlogloss:0.23235\n",
      "[170]\ttrain-mlogloss:0.22926\teval-mlogloss:0.23235\n",
      "[171]\ttrain-mlogloss:0.22923\teval-mlogloss:0.23236\n",
      "[172]\ttrain-mlogloss:0.22921\teval-mlogloss:0.23236\n",
      "[173]\ttrain-mlogloss:0.22919\teval-mlogloss:0.23236\n",
      "[174]\ttrain-mlogloss:0.22916\teval-mlogloss:0.23237\n",
      "[175]\ttrain-mlogloss:0.22914\teval-mlogloss:0.23238\n",
      "[176]\ttrain-mlogloss:0.22912\teval-mlogloss:0.23238\n",
      "[177]\ttrain-mlogloss:0.22910\teval-mlogloss:0.23239\n",
      "[178]\ttrain-mlogloss:0.22908\teval-mlogloss:0.23239\n",
      "[179]\ttrain-mlogloss:0.22906\teval-mlogloss:0.23240\n",
      "[180]\ttrain-mlogloss:0.22904\teval-mlogloss:0.23241\n",
      "[181]\ttrain-mlogloss:0.22903\teval-mlogloss:0.23241\n",
      "[182]\ttrain-mlogloss:0.22901\teval-mlogloss:0.23242\n",
      "[183]\ttrain-mlogloss:0.22899\teval-mlogloss:0.23243\n",
      "[184]\ttrain-mlogloss:0.22897\teval-mlogloss:0.23243\n",
      "[185]\ttrain-mlogloss:0.22895\teval-mlogloss:0.23243\n",
      "[186]\ttrain-mlogloss:0.22893\teval-mlogloss:0.23244\n",
      "[187]\ttrain-mlogloss:0.22891\teval-mlogloss:0.23245\n",
      "[188]\ttrain-mlogloss:0.22889\teval-mlogloss:0.23245\n",
      "[189]\ttrain-mlogloss:0.22888\teval-mlogloss:0.23246\n",
      "[190]\ttrain-mlogloss:0.22886\teval-mlogloss:0.23246\n",
      "[191]\ttrain-mlogloss:0.22884\teval-mlogloss:0.23247\n",
      "[192]\ttrain-mlogloss:0.22882\teval-mlogloss:0.23248\n",
      "[193]\ttrain-mlogloss:0.22880\teval-mlogloss:0.23249\n",
      "[194]\ttrain-mlogloss:0.22878\teval-mlogloss:0.23249\n",
      "[195]\ttrain-mlogloss:0.22876\teval-mlogloss:0.23250\n",
      "[196]\ttrain-mlogloss:0.22875\teval-mlogloss:0.23251\n",
      "[197]\ttrain-mlogloss:0.22873\teval-mlogloss:0.23251\n",
      "[198]\ttrain-mlogloss:0.22871\teval-mlogloss:0.23251\n",
      "[199]\ttrain-mlogloss:0.22870\teval-mlogloss:0.23251\n",
      "[200]\ttrain-mlogloss:0.22869\teval-mlogloss:0.23251\n",
      "[201]\ttrain-mlogloss:0.22868\teval-mlogloss:0.23251\n",
      "[202]\ttrain-mlogloss:0.22866\teval-mlogloss:0.23252\n",
      "[203]\ttrain-mlogloss:0.22864\teval-mlogloss:0.23252\n",
      "[204]\ttrain-mlogloss:0.22862\teval-mlogloss:0.23253\n",
      "[205]\ttrain-mlogloss:0.22861\teval-mlogloss:0.23253\n",
      "[206]\ttrain-mlogloss:0.22860\teval-mlogloss:0.23254\n",
      "[207]\ttrain-mlogloss:0.22858\teval-mlogloss:0.23254\n",
      "[208]\ttrain-mlogloss:0.22857\teval-mlogloss:0.23254\n",
      "[209]\ttrain-mlogloss:0.22855\teval-mlogloss:0.23255\n",
      "[210]\ttrain-mlogloss:0.22853\teval-mlogloss:0.23256\n",
      "[211]\ttrain-mlogloss:0.22852\teval-mlogloss:0.23256\n",
      "[212]\ttrain-mlogloss:0.22850\teval-mlogloss:0.23257\n",
      "[213]\ttrain-mlogloss:0.22849\teval-mlogloss:0.23258\n",
      "[214]\ttrain-mlogloss:0.22847\teval-mlogloss:0.23258\n",
      "[215]\ttrain-mlogloss:0.22846\teval-mlogloss:0.23259\n",
      "[216]\ttrain-mlogloss:0.22844\teval-mlogloss:0.23260\n",
      "[217]\ttrain-mlogloss:0.22843\teval-mlogloss:0.23260\n",
      "[218]\ttrain-mlogloss:0.22842\teval-mlogloss:0.23260\n",
      "[219]\ttrain-mlogloss:0.22840\teval-mlogloss:0.23260\n",
      "[220]\ttrain-mlogloss:0.22839\teval-mlogloss:0.23260\n",
      "[221]\ttrain-mlogloss:0.22837\teval-mlogloss:0.23261\n",
      "[222]\ttrain-mlogloss:0.22835\teval-mlogloss:0.23262\n",
      "[223]\ttrain-mlogloss:0.22834\teval-mlogloss:0.23262\n",
      "[224]\ttrain-mlogloss:0.22833\teval-mlogloss:0.23263\n",
      "[225]\ttrain-mlogloss:0.22831\teval-mlogloss:0.23263\n",
      "[226]\ttrain-mlogloss:0.22830\teval-mlogloss:0.23263\n",
      "[227]\ttrain-mlogloss:0.22829\teval-mlogloss:0.23264\n",
      "[228]\ttrain-mlogloss:0.22828\teval-mlogloss:0.23265\n",
      "[229]\ttrain-mlogloss:0.22827\teval-mlogloss:0.23265\n",
      "[230]\ttrain-mlogloss:0.22826\teval-mlogloss:0.23265\n",
      "[231]\ttrain-mlogloss:0.22824\teval-mlogloss:0.23265\n",
      "[232]\ttrain-mlogloss:0.22823\teval-mlogloss:0.23265\n",
      "[233]\ttrain-mlogloss:0.22822\teval-mlogloss:0.23266\n",
      "[234]\ttrain-mlogloss:0.22821\teval-mlogloss:0.23266\n",
      "[235]\ttrain-mlogloss:0.22820\teval-mlogloss:0.23267\n",
      "[236]\ttrain-mlogloss:0.22819\teval-mlogloss:0.23267\n",
      "[237]\ttrain-mlogloss:0.22817\teval-mlogloss:0.23268\n",
      "[238]\ttrain-mlogloss:0.22816\teval-mlogloss:0.23269\n",
      "[239]\ttrain-mlogloss:0.22815\teval-mlogloss:0.23269\n",
      "[240]\ttrain-mlogloss:0.22813\teval-mlogloss:0.23269\n",
      "[241]\ttrain-mlogloss:0.22812\teval-mlogloss:0.23269\n",
      "[242]\ttrain-mlogloss:0.22811\teval-mlogloss:0.23269\n",
      "[243]\ttrain-mlogloss:0.22809\teval-mlogloss:0.23270\n",
      "[244]\ttrain-mlogloss:0.22808\teval-mlogloss:0.23271\n",
      "[245]\ttrain-mlogloss:0.22806\teval-mlogloss:0.23272\n",
      "[246]\ttrain-mlogloss:0.22806\teval-mlogloss:0.23272\n",
      "[247]\ttrain-mlogloss:0.22804\teval-mlogloss:0.23272\n",
      "[248]\ttrain-mlogloss:0.22803\teval-mlogloss:0.23273\n",
      "[249]\ttrain-mlogloss:0.22802\teval-mlogloss:0.23273\n",
      "[250]\ttrain-mlogloss:0.22801\teval-mlogloss:0.23274\n",
      "[251]\ttrain-mlogloss:0.22799\teval-mlogloss:0.23274\n",
      "[252]\ttrain-mlogloss:0.22798\teval-mlogloss:0.23275\n",
      "[253]\ttrain-mlogloss:0.22797\teval-mlogloss:0.23276\n",
      "[254]\ttrain-mlogloss:0.22796\teval-mlogloss:0.23276\n",
      "[255]\ttrain-mlogloss:0.22794\teval-mlogloss:0.23276\n",
      "[256]\ttrain-mlogloss:0.22793\teval-mlogloss:0.23276\n",
      "[257]\ttrain-mlogloss:0.22792\teval-mlogloss:0.23276\n",
      "[258]\ttrain-mlogloss:0.22791\teval-mlogloss:0.23276\n",
      "[259]\ttrain-mlogloss:0.22790\teval-mlogloss:0.23277\n",
      "Train AUC Score 0.6242376758160719\n",
      "Test AUC Score 0.5800548734952883\n"
     ]
    }
   ],
   "source": [
    "# XGBoost\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data_1, label, test_size=0.4, random_state=0)\n",
    "X_tset, X_valid, y_test, y_valid = train_test_split(X_test,y_test,test_size=0.5,random_state=0)\n",
    "clf = xgb\n",
    "train_matrix = clf.DMatrix(X_train, label=y_train, missing=-1)\n",
    "# 假设您需要将 X_test 切片以匹配 y_test 的长度\n",
    "X_test_corrected = X_test.iloc[:51428]\n",
    "# 确保 y_test 是一维数组\n",
    "y_test_values = y_test.values\n",
    "# 使用切片后的 X_test_corrected 来创建 test_matrix\n",
    "test_matrix = clf.DMatrix(X_test_corrected, label=y_test_values, missing=-1)\n",
    "z = clf.DMatrix(X_valid,label=y_valid,missing=-1)\n",
    "params = {'booster':'gbtree',\n",
    "          'objective':'multi:softprob',\n",
    "          'eval_metric':'mlogloss',\n",
    "          'gamma':1,\n",
    "          'min_child_weight':1.5,\n",
    "          'max_depth':5,\n",
    "          'lambda':100,\n",
    "          'subsample':0.7,\n",
    "          'colsample_bytree':0.7,\n",
    "          'colsample_bylevel':0.7,\n",
    "          'eta':0.03,\n",
    "          'tree_method':'exact',\n",
    "          'seed':2017,\n",
    "          \"num_class\":2\n",
    "         }\n",
    "score_dict={}\n",
    "num_round = 10000\n",
    "early_stopping_rounds = 100\n",
    "watchlist =[(train_matrix, 'train'),\n",
    "            (test_matrix,'eval')\n",
    "           ]\n",
    "model = clf. train(params,\n",
    "                   train_matrix,\n",
    "                   num_boost_round=num_round,#训练的轮数，即生成的树的数号\n",
    "                   evals=watchlist,#训练时用于评估模型的数据集合，用户可以通过验证集来评估模型好坏\n",
    "                   early_stopping_rounds=early_stopping_rounds,\n",
    "                   #激活早停止当验证的错误率early_stopping_rounds轮未下降则停止训练\n",
    "                   #如果发生了早停止，则模型会提供三个额外的字段:bst.best_score,bst.best_iteration 和 bst. best_ntree_limit\n",
    "                   evals_result =score_dict\n",
    "                  )\n",
    "y_train_pred = model.predict(train_matrix)#ntree_limit 用于预测的树的数量\n",
    "y_train_pred_pos= y_train_pred[:,1]\n",
    "\n",
    "y_test_pred = model.predict(z)\n",
    "y_test_pred_pos = y_test_pred[:, 1]\n",
    "\n",
    "auc_train = roc_auc_score(y_train, y_train_pred_pos) #AUC评分\n",
    "auc_test = roc_auc_score(y_valid, y_test_pred_pos)\n",
    "\n",
    "print(f\"Train AUC Score {auc_train}\")\n",
    "print(f\"Test AUC Score {auc_test}\")\n",
    "fpr,tpr,_=roc_curve(y_valid,y_test_pred_pos)#绘制ROC曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "aff2cca4-cc33-4d5a-8a63-50cd2cf88393",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_columns = [col for col in train_data.columns if col not in ['user id','label']]\n",
    "train_data_1 = train_data[features_columns]\n",
    "test_data_1 = test_data[features_columns]\n",
    "target = train_data['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b6bc2817-ccd6-4f43-9e19-9ad747da7ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(train_1,target_1):\n",
    "    X_train, X_val, y_train, y_val = train_test_split(train_1, target_1, test_size = 0.2 ,random_state = 42)\n",
    "    clf = XGBClassifier()\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    y_train_pred = clf.predict_proba(X_train)\n",
    "    y_train_pred_pos = y_train_pred[:, 1]\n",
    "\n",
    "    y_val_pred = clf.predict_proba(X_val)\n",
    "    y_val_pred_pos = y_val_pred[:, 1]\n",
    "\n",
    "    auc_train = roc_auc_score(y_train, y_train_pred_pos)\n",
    "    auc_test=roc_auc_score(y_val,y_val_pred_pos)\n",
    "    \n",
    "    print(f\"Train AUC Score {auc_train}\")\n",
    "    print(f\"Test AUC Score {auc_test}\")\n",
    "    \n",
    "    fpr, tpr,_ = roc_curve(y_val, y_val_pred_pos)\n",
    "    return fpr, tpr, clf, auc_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6e7ac191-3ff5-43cd-812e-55b4a1fd2705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.8287364154119496\n",
      "Test AUC Score 0.5961925754763462\n"
     ]
    }
   ],
   "source": [
    "fpr_1,tpr_1,clf_1,auc_test_1 = model(train_data_1,target)#0.6153997"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6893d657-8fa0-495f-a3b4-986d515963cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_selection(train, train_sel, target):\n",
    "    clf = XGBClassifier ()\n",
    "    scores = cross_val_score(clf, train, target,cv=5)\n",
    "    scores_sel = cross_val_score(clf, train_sel, target, cv=5)\n",
    "    \n",
    "    print(\"No Select Accuracy:%0.2f(+/-%0.2f)\" % (scores.mean(), scores.std()* 2))\n",
    "    print(\"Features Select Accuracy::%0.2f(+/-%0.2f)\" % (scores_sel.mean(), scores_sel.std()* 2))\n",
    "    return scores.mean(), scores_sel.mean ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1c380e26-ed95-419a-a149-2aad452be7b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def select(train, goal):\n",
    "    a_score=[]\n",
    "    b_score=[]\n",
    "    for i in range(2, train. shape[1]+1):\n",
    "        sel = SelectKBest(mutual_info_classif, k=i)\n",
    "        sel = sel.fit(train, goal)\n",
    "        train_sel = sel. transform(train)\n",
    "        \n",
    "        print('训练数据未特征筛选维度',train.shape)\n",
    "        print('训练数据特征筛选维度后',train_sel.shape)\n",
    "        mean_train,mean_test=feature_selection(train, train_sel, goal)\n",
    "        a_score.append (mean_train)\n",
    "        b_score.append(mean_test)\n",
    "    x=list (range(2, train.shape[1]+1))\n",
    "    plt.plot(x,a_score,marker='o',markersize=3) #绘制折线图，添加数据点，设置点的大小\n",
    "    plt.plot(x,b_score,marker='o',markersize=3)\n",
    "    plt.xticks(x)\n",
    "    plt.show()\n",
    "    return a_score,b_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6c8f4312-d569-461b-89c7-6feb6ea30609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 2)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 3)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 4)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 5)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 6)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 7)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 8)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 9)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 10)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 11)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 12)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 13)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 14)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 15)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 16)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 17)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 18)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 19)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 20)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 21)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n",
      "训练数据未特征筛选维度 (257141, 22)\n",
      "训练数据特征筛选维度后 (257141, 22)\n",
      "No Select Accuracy:0.94(+/-0.00)\n",
      "Features Select Accuracy::0.94(+/-0.00)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_score,b_score=select(train_data_1,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "78702f8f-7af8-4614-9ff4-7b02e9a4bedb",
   "metadata": {},
   "outputs": [],
   "source": [
    " def select_fin(i,train_1,target_1):\n",
    "     sel=SelectKBest(mutual_info_classif, k=i)\n",
    "     sel=sel.fit(train_1, target_1)\n",
    "     train_sel=sel.transform(train_1)\n",
    "     test_sel=sel.transform(test_data_1)\n",
    "     return train_sel,test_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c3676793-2145-439a-a27f-af37acb8cdc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sel, test_sel=select_fin(4, train_data_1, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d40198d9-6cb1-44ee-a12d-d661811e1ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC Score 0.6759537646708762\n",
      "Test AUC Score 0.6019406551175617\n"
     ]
    }
   ],
   "source": [
    "fpr_5,tpr_5,clf_5,auc_test_5=model(train_sel,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "39210015-eedb-4237-b980-6d4330e73d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelfit(alg,X,y,useTrainCV=True,cv_folds=5, early_stopping_rounds=50):\n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()#参数\n",
    "        xgtrain=xgb.DMatrix(X, label=y)#训练数据\n",
    "        cvresult=xgb.cv(xgb_raram,xgtrain,num_boost_round=alg.get_params()['n_estimators'],\n",
    "                       nfold=cv_folds,metrics='auc',early_stopping_rounds=early_stopping_rounds)\n",
    "        \n",
    "        #num_boost_round:数的个数 nfold几折交叉验证 early_stopping_rounds 早停值\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "        #输出树的个数\n",
    "    #在数据上拟合算法\n",
    "    alg.fit(X, y,eval_metric='auc')\n",
    "\n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(X)\n",
    "    dtrain_predprob = alg. predict_proba(X)[:, 1]\n",
    "    \n",
    "    #Print model report:\n",
    "    print(\"\\nModel Report\")\n",
    "    print(\"Accuracy:%.4g\" % metrics.accuracy_score(y,dtrain_predictions))\n",
    "    print(\"AUC Score (Train): %f\" % metrics.roc_auc_score(y,dtrain_predprob))\n",
    "    \n",
    "    feat_imp = pd.Series(alg.get_booster().get_fscore()).sort_values(ascending=False)\n",
    "    #bosster()改为get_bosster()\n",
    "    feat_imp.plot(kind='bar',title='Feature Importances')\n",
    "    plt.ylabel('Feature Importance Score')\n",
    "    plt.show()\n",
    "    print('n_estimators=',cvresult.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6eab3143-fa1b-42a6-a576-c5c6aba91241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'mean_fit_time': array([35.42946696, 38.54965582, 44.63061957, 40.36561933, 41.21381855,\n",
       "         46.16503549, 24.13381705]),\n",
       "  'std_fit_time': array([1.49152353, 4.71345264, 0.51262777, 2.14759038, 2.87308051,\n",
       "         0.33630704, 9.37438862]),\n",
       "  'mean_score_time': array([0.44692383, 0.50920563, 0.58949494, 0.35619178, 0.47855096,\n",
       "         0.44993935, 0.25079188]),\n",
       "  'std_score_time': array([0.09935752, 0.10625895, 0.13414719, 0.06852901, 0.14515485,\n",
       "         0.1039393 , 0.07548243]),\n",
       "  'param_reg_alpha': masked_array(data=[1e-05, 0.0001, 0.001, 0.01, 0.1, 1.0, 100.0],\n",
       "               mask=[False, False, False, False, False, False, False],\n",
       "         fill_value=1e+20),\n",
       "  'params': [{'reg_alpha': 1e-05},\n",
       "   {'reg_alpha': 0.0001},\n",
       "   {'reg_alpha': 0.001},\n",
       "   {'reg_alpha': 0.01},\n",
       "   {'reg_alpha': 0.1},\n",
       "   {'reg_alpha': 1},\n",
       "   {'reg_alpha': 100}],\n",
       "  'split0_test_score': array([0.61161258, 0.61161279, 0.61172394, 0.61129703, 0.61380659,\n",
       "         0.61221547, 0.59962546]),\n",
       "  'split1_test_score': array([0.61953617, 0.61953644, 0.62111139, 0.62052167, 0.62047112,\n",
       "         0.61834658, 0.60922137]),\n",
       "  'split2_test_score': array([0.62027784, 0.62027795, 0.61945096, 0.61774491, 0.62068156,\n",
       "         0.61906244, 0.60887072]),\n",
       "  'split3_test_score': array([0.62175514, 0.6217551 , 0.61896521, 0.6222694 , 0.62021042,\n",
       "         0.62125336, 0.60626984]),\n",
       "  'split4_test_score': array([0.61560877, 0.61560884, 0.61499538, 0.6148699 , 0.61669476,\n",
       "         0.61495061, 0.59799108]),\n",
       "  'mean_test_score': array([0.6177581 , 0.61775822, 0.61724938, 0.61734058, 0.61837289,\n",
       "         0.61716569, 0.60439569]),\n",
       "  'std_test_score': array([0.0036844 , 0.00368435, 0.00341518, 0.0039295 , 0.00271206,\n",
       "         0.00319726, 0.00470318]),\n",
       "  'rank_test_score': array([3, 2, 5, 4, 1, 6, 7])},\n",
       " {'reg_alpha': 0.1},\n",
       " 0.6183728888534787)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对模型进行超参数调优\n",
    "#树的最大深度 最小叶子节点样本权重\n",
    "param_test1 = {\n",
    "    'max_depth':range(3,10,1),\n",
    "    'min_child_weight':range(1,6,1)\n",
    "}\n",
    "gsearch1 = GridSearchCV(estimator = XGBClassifier(learning_rate = 0.1,n_estimators=333, max_depth=5,\n",
    "min_child_weight=1, gamma=0,subsample=0.8, colsample_bytree=0.8,\\\n",
    " objective='binary:logistic',nthread=8,scale_pos_weight=1,seed=27),\n",
    " param_grid=param_test1,scoring='roc_auc',n_jobs=-1,cv=5)\n",
    "gsearch1.fit(train_data_1, target)\n",
    "gsearch1.cv_results_, gsearch1.best_params_, gsearch1.best_score_\n",
    "\n",
    "param_test3 ={\n",
    "    'gamma':[i / 10.0 for i in range(0,5)]\n",
    "}\n",
    "gsearch3 = GridSearchCV(\n",
    "    estimator=XGBClassifier(learning_rate=0.1,n_estimators=333,max_depth=5,min_child_weight=2, gamma=0,\n",
    "                            subsample=0.8, colsample_bytree=0.8, objective='binary:logistic',nthread=8,\n",
    "                            scale_pos_weight=1, seed=27), param_grid=param_test3,scoring='roc_auc', n_jobs=-1,\n",
    "    cv=5)\n",
    "gsearch3.fit(train_data_1,target)\n",
    "gsearch3.cv_results_,gsearch3.best_params_, gsearch3.best_score_\n",
    "\n",
    "param_test4={\n",
    "    'subsample':[i/10.0 for i in range(6,10)],\n",
    "    'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
    "}\n",
    "\n",
    "gsearch4 = GridSearchCV(\n",
    "    estimator=XGBClassifier(learning_rate=0.1,n_estimators=333,max_depth=5,min_child_weight=2, gamma=0.0,\n",
    "                            subsample=0.8,colsample_bytree=0.8, objective='binary:logistic', nthread=8,\n",
    "                            scale_pos_weight=1,seed=27),param_grid=param_test4, scoring='roc_auc', n_jobs=-1,\n",
    "    cv=5)\n",
    "\n",
    "gsearch4.fit(train_data_1,target)\n",
    "gsearch4.cv_results_,gsearch4.best_params_, gsearch4.best_score_\n",
    "\n",
    "param_test6={\n",
    "    'reg_alpha':[1e-5,1e-4,1e-3,1e-2,0.1,1,100]\n",
    "}\n",
    "gsearch6 = GridSearchCV(estimator = XGBClassifier(learning_rate = 0.1, n_estimators=333, max_depth=5, min_child_weight=2,\n",
    "                                                  gamma=0.0,subsample=0.9, colsample_bytree=0.7,objective='binary:logistic', nthread=8,\n",
    "                                                  scale_pos_weight=1,seed=27),param_grid = param_test6, scoring='roc_auc',n_jobs=-1, cv=5)\n",
    "gsearch6.fit (train_data_1, target)\n",
    "gsearch6.cv_results_,gsearch6.best_params_, gsearch6.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "54870483-30f4-46d6-a633-49680fdb5e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#  best_xgb 是训练好的 XGBClassifier 模型\n",
    "# test_data_1 是包含 user_id 和 merchant_id 的测试数据 DataFrame\n",
    "\n",
    "# 获取正类的概率\n",
    "probabilities = best_xgb.predict_proba(test_data_1)[:, 1]\n",
    "\n",
    "# 从 test_data_1 DataFrame 中提取 user_id 和 merchant_id 列\n",
    "test_user_ids = test_data_1['user_id']\n",
    "test_merchant_ids = test_data_1['merchant_id']\n",
    "\n",
    "# 构建包含 user_id, merchant_id 和 prob 字段的 DataFrame\n",
    "predictions_df = pd.DataFrame({\n",
    "    'user_id': test_user_ids,\n",
    "    'merchant_id': test_merchant_ids,\n",
    "    'prob': probabilities\n",
    "})\n",
    "\n",
    "# 将 DataFrame 保存到 CSV 文件\n",
    "predictions_df.to_csv('D:\\\\机器学习\\\\天猫复购预测\\\\data_format1\\\\data_format1\\\\prediction.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8511acc-acbc-4c2e-ab8e-f651fe489c9d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
